DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendment filed on 12/28/2025. Claims 1-8 remain pending in the application. Claims 1-4 are independent.
Drawings
Applicant's amendment to the specification corrects previous objections; therefore, the previous objections are withdrawn.
Claim Objections
Applicant's amendment to claims corrects previous objections; therefore, the previous objections are withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over JE et al. (US 2023/0014613 A1, filed on 11/25/2020), hereinafter JE in view of LEE (US 2025/0055739 A1, filed on 12/22/2021), hereinafter LEE.
Independent Claim 1
JE discloses a terminal device (JE, ¶¶ [0017], [0050], and [0068] with 120 in FIG. 1: user equipment (UE) or mobile station (MS)) comprising:
a reception unit (JE, ¶¶ [0437]-[0442] with 1801 in FIG. 18: the communication unit 1801 performs functions for transmitting and receiving a signal through a radio channel; some of the communication unit 1801 may be referred to as a "receiver" or a "transceiver") that receives a signal for instructing start of training (JE, ¶¶ [0389]-[00391], [0050], and [0071] with 1620 in FIG. 16A: Learning for AI-Based Handover (HO); FIG. 16A illustrates an example of operations of the BS and the UE for learning; in operation 1620, the UE may receive feedback configuration information from the base station (BS); the feedback configuration information indicates a return of learning data through a feedback request value within handover indication information (HOinformationMessage); the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; ¶¶ [0409] and [0017] with 1660 in FIG. 16A: in operation 1660, the BS may learn a neural network (NN) model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0411] with 1670 in FIG. 16B: in operation 1670, the UE may receive feedback configuration information from the BS; ¶ [0413] with 16130 in FIG. 16B: when receiving a message including the learning data from the target BS, the source BS may learn the NN and the W for the AI HO in operation 16130; i.e., UE receives a instructing signal from BS to collect training data for learning/training NN model); and
a transmission unit (JE, ¶¶ [0437]-[0442] with 1801 in FIG. 18: the communication unit 1801 performs functions for transmitting a signal through a radio channel; some of the communication unit 1801 may be referred to as a "transmitter" or a "transceiver") that transmits training data (JE, ¶¶ [0391] and [0408] with 1650 in FIG. 16A: the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; In operation 1650, the UE may feedback training data for the AI-based handover to the serving BS according to a format configured by the feedback configuration information; ¶ [0413] with 16100 and 16110 in FIG. 16B: the handover may occur while learning data is collected like in operation 1690, and thus the handover to the target BS is made; in operation 16110, the UE may transmit collected learning data to the target BS which is which is the current serving cell, not to the source BS in this case; when receiving the corresponding message, the target BS may identify an ID of the source BS (or source cell) within the message; since the ID is different from the ID of the target BS, the target BS may feedback learning data to another BS; in operation 16120, the target BS may transmit the learning data to the source BS),
wherein the signal for instructing the start of the training includes a training start timing and a training period (JE, ¶¶ [0391]-[0405] with 1620 in FIG. 16A: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: (a) Neural Network (NN) input info; (b) NN output info; (c) Performance; (d) training data period: NN learning data collection period; (e) training data start time: NN learning data collection start time point; and (f) training data end time: NN learning data collection end time point), and
in a case where the signal for instructing the start of the training is received, the training data is generated based on the training start timing and the training period (JE, ¶¶ [0406]-[0407] with 1630 and 1640 in FIG. 16A: in operation 1630, the UE may collect training data on the basis of feedback configuration information; the UE records input info of the NN and output info of the NN in every training data period from a training data start time to a training data end time, and the output info of the NN configures the right answer based on performance; in operation 1640, the UE may detect expiration of the learning section; the BS may learn and update the structure information (NN) and the weight information (W) of the neural network model for the AI-based handover; ¶¶ [0412]-[0413] with 1680 and 16100 in FIG.16B: in operation 1680, the UE may acquire learning data on the basis of the feedback configuration information; the UE may detect expiration of the learning section in operation 16100; ¶ [0202]: a training data period means a period of recording of NN learning data (input value/output value); a training data start time means an NN learning data collection start time point and a training data end time means an NN learning data collection end time point; the UE records an input value and an output value measured in units of training data periods between the training data start time and the training data end time; the feedback format is an example of transmitting information for learning the NN, and may mean a set of data for learning the NN through the input value/output value),
wherein the training data is generated based on a reference signal transmitted from a base station apparatus (JE, ¶¶ [0164]-[0165]: a channel quality may be acquired by measuring a received signal; hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal to noise ratio (SNR), signal to interference and noise ratio (SINR), carrier to interference and noise ratio (CINR), error vector magnitude (EVM), bit error rate (BER), block error rate (BLER), other terms having the technical meaning equivalent thereto, or indexes indicating a channel quality may be additionally used; ; ¶¶ [0170] and [0179]-[0201]: the UE receives handover-related reference information from the BS; the BS may transmit information related to the handover (hereinafter, referred to as handover information) to the UE in operation 620; the handover information may include NN input info: input value information of HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like); a feedback request is a value of which the BS informs in order to allow the UE to transmit learning information related to the handover for learning; the feedback format indicates a transmission format in which the UE feeds back handover learning data; the feedback format indicates which information is fed back for AI learning; NN input info is input value information of the handover NN used for the handover; e.g., the radio signal intensity such as RSRP, RSRQ, or SINR may be used as the input value; the input value may include the radio signal intensity and the throughput; when maximization of an RSRP value for a predetermined time period is aimed, the UE may add RSRP values of each BS for a predetermined time period after the handover and generate learning data to output a maximum BS(cell); ¶¶ [0391]-[0405]: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: NN input info: input value information of the HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like), throughput, an SRS, and a wireless signal which can be measured by the UE such as cqi and the like), and the training data is usable by the terminal device for handover by artificial intelligence and/or machine learning (JE, ¶¶ [0153]-[0154] and [0162]-[0164]: the AI-based handover procedure may include a scheme for transmitting the configured neural network model to the UE, a procedure for making a request for a handover to a target cell identified according to the configured model to a serving cell, or a learning procedure for updating the configured neural network mode; as a better quality of data is accumulated and construction of a neural network model which is a determination reference is more consistent with reality, a handover determination based on an AI algorithm may be more accurate for a situation that the UE or the BS individually faces; the neural network for the AI-based handover may require a plurality of input values; e.g., the neural network for the AI-based handover may consider a channel quality of the current serving cell, a channel quality of the target cell, and a type of the serving cell (e.g., whether the serving cell is a small cell or an RAT type) as an input; further, the neural network for the AI-based handover may provide a plurality of output values; e.g. the neural network for the AI-based handover may indicate a plurality of target cells to which the handover can be performed; an efficient handover may be achieved by applying an AI-based handover determination method to a wireless communication system between the BS and the UE through a combination of cell information and measurement information or environment information of the UE).
JE fails to explicitly disclose wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning.
LEE teaches a system and a method relating to machine learning in a wireless communication (LEE, ¶¶ [0002] and [0006]), wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning (LEE, ¶¶ [0100]-[0106]: the 6G system will support AI for full automation; advance in machine learning will create a more intelligent network for real-time communication in 6G; when AI is introduced to communication, real-time data transmission may be simplified and improve; AI may determine a method of performing complicated target tasks using countless analysis; i.e., AI may increase efficiency and reduce processing delay; time-consuming tasks such as handover, network selection or resource scheduling may be immediately performed by using AI; deep learning have been focused on the wireless resource management and allocation field; combine deep learning in the physical layer with wireless transmission are emerging; AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism; e.g., channel coding and decoding based on deep learning, signal estimation and detection based on deep learning, multiple input multiple output (MIMO) mechanisms based on deep learning, resource scheduling and allocation based on AI, etc. may be included; machine learning may be used for channel estimation and channel tracking and may be used for power allocation, interference cancellation, etc. in the physical layer of DL; in addition, machine learning may be used for antenna selection, power control, symbol detection, etc. in the MIMO system; deep learning-based AI algorithms require a lot of training data in order to optimize training parameters; however, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline; static training for training data in a specific channel environment may cause a contradiction between the diversity and dynamic characteristics of a radio channel; ¶¶ [0134]-[0144] with FIGS. 15A-C: it is not easy to collect a lot of data for training the neural network in a communication channel with a long-tail distribution; even if a terminal moves to experience a new channel, collects data and performs fine-tuning training for an offline-pretrained neural network again, the terminal can hardly reflect a new change sufficiently; neural network training should reflect effect of a channel between a transmitter and a receiver; a reference signal or a pilot signal may be used as training data to reflect a channel effect; e.g., a device may be subject to transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception; a neural network requirement technique is needed to enable a device to secure as many reference signal wireless resources as possible and to have fast learning in a propagation environment with a long-tail distribution; proposes a communication system, a communication procedure, and a signaling method, which are based on online meta learning; meta learning uses a neural network that is pretrained based on various tasks; meta learning is learning that uses such a pretrained neural network to enable a device to well perform inference like regression and classification for a new task; i.e., meta learning is a method of enhancing learning and estimation performance for a new task and is learning for learning (learn-to-learn); when a device experiences a new task, the device relearns a model parameter ϕ suitable for the new task from the meta parameter θ and performs inference based on the relearning; ¶¶ [0145]-[0149] with FIG. 16: proposes a meta learning method for a transmitting/receiving task associated with a reference signal; there are various reference signals; e.g., (a) a device may transmit a phase-tracking reference signal with a specific pattern in order to correct a phase error in a high-frequency band; (b) the device may transmit a demodulation reference signal (DM-RS) with a specific pattern in order to estimate a channel and to estimate data; (c) the device may transmit a channel state information-reference signal (CSI-RS) with a specific pattern in order to track time synchronization of a channel, to track a beam, and to find out channel quality information like a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI); (d) the device may transmit a sounding reference signal (SRS) with a specific pattern p for channel sounding; and (e) the terminal may transmit a positioning reference signal (PRS) for measuring a location; the device may transmit various types of reference signals according to purposes; define transmission/reception and measurement of reference signals as tasks so that a device may use a wireless resource as efficiently as possible; applying meta learning to transmission/reception and measurement of reference signals; the task may mean transmission/reception and measurement related to all reference signals that have passed a channel at time t, wherein the reference signals may include a CSI-RS, a PTRS, a PRS, a CRS, and a DMRS; in each task, a result of passing a channel may be used as a dataset; e.g., a terminal may perform a task of measuring a CQI based on a CSI-RS, wherein in case the terminal succeeds in CQI measurement based on the CSI-RS, a result may be used as one supervised learning dataset; online meta learning proposed may be divided mainly into two parts as follows: (a) meta-training is a procedure in which a device learns an optimal meta parameter θ based on a meta-training data set of every reference signal; and (b) adaptation is a procedure in which the device learns ϕ by learning based on the optimal meta parameter θ* to succeed in a task for a specific reference signal and performs a task related to the reference signal at the same time; in case the device succeeds in adaptation of the reference signal task, the device may obtain a reference signal dataset and a parameter ϕ for the task; when the device acquires θ*, meta training may be performed by inputting the reference signal dataset and the task parameter obtained from the adaptation; in addition, the device may acquire ϕ by performing a specific task based on θ* obtained from the meta training, thereby performing adaptation).
JE and LEE are analogous art because they are from the same field of endeavor, a system and a method relating to machine learning in a wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of LEE to JE. Motivation for doing so would easily expand AI techniques in a new task for increasing efficiency and reducing processing delay (LEE, ¶¶ [0100]-[0103], [0134]-[0137], and [0144]).
Independent Claim 2
JE discloses a base station apparatus (JE, ¶¶ [0016], [0050], and [0067] with 110 in FIG. 1: base station (BS)) comprising:
a transmission unit (JE, ¶¶ [0423]-[0428] with 1701 in FIG. 17: the wireless communication unit 1701 performs functions for transmitting a signal through a radio channel; all or some of the wireless communication unit 1701 may be referred to as a 'transmitter' or a 'transceiver') that transmits a signal for instructing start of training (JE, ¶¶ [0389]-[00391], [0050], and [0071] with 1620 in FIG. 16A: Learning for AI-Based Handover (HO); FIG. 16A illustrates an example of operations of the BS and the UE for learning; in operation 1620, the UE may receive feedback configuration information from the base station (BS); the feedback configuration information indicates a return of learning data through a feedback request value within handover indication information (HOinformationMessage); the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; ¶¶ [0409] and [0017] with 1660 in FIG. 16A: in operation 1660, the BS may learn a neural network (NN) model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0411] with 1670 in FIG. 16B: in operation 1670, the UE may receive feedback configuration information from the BS; ¶ [0413] with 16130 in FIG. 16B: when receiving a message including the learning data from the target BS, the source BS may learn the NN and the W for the AI HO in operation 16130; i.e., UE receives a instructing signal from BS to collect training data for learning/training NN model); and
a reception unit (JE, ¶¶ [0423]-[0428] with 1701 in FIG. 17: the wireless communication unit 1701 performs functions for receiving a signal through a radio channel; all or some of the wireless communication unit 1701 may be referred to as a 'receiver' or a 'transceiver') that receives training data (JE, ¶¶ [0391] and [0408] with 1650 in FIG. 16A: the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; In operation 1650, the UE may feedback training data for the AI-based handover to the serving BS according to a format configured by the feedback configuration information; ¶ [0413] with 16100 and 16110 in FIG. 16B: the handover may occur while learning data is collected like in operation 1690, and thus the handover to the target BS is made; in operation 16110, the UE may transmit collected learning data to the target BS which is which is the current serving cell, not to the source BS in this case; when receiving the corresponding message, the target BS may identify an ID of the source BS (or source cell) within the message; since the ID is different from the ID of the target BS, the target BS may feedback learning data to another BS; in operation 16120, the target BS may transmit the learning data to the source BS),
wherein the signal for instructing the start of the training includes a training start timing and a training period (JE, ¶¶ [0391]-[0405] with 1620 in FIG. 16A: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: (a) Neural Network (NN) input info; (b) NN output info; (c) Performance; (d) training data period: NN learning data collection period; (e) training data start time: NN learning data collection start time point; and (f) training data end time: NN learning data collection end time point), and
in a case where the signal for instructing the start of the training is transmitted, the training data is instructed to be generated based on the training start timing and the training period (JE, ¶¶ [0406]-[0407] with 1630 and 1640 in FIG. 16A: in operation 1630, the UE may collect training data on the basis of feedback configuration information; the UE records input info of the NN and outinfo of the NN in every training data period from a training data start time to a training data end time, and the output info of the NN configures the right answer based on performance; in operation 1640, the UE may detect expiration of the learning section; the BS may learn and update the structure information (NN) and the weight information (W) of the neural network model for the AI-based handover; ¶¶ [0412]-[0413] with 1680 and 16100 in FIG.16B: in operation 1680, the UE may acquire learning data on the basis of the feedback configuration information; the UE may detect expiration of the learning section in operation 16100; ¶ [0202]: a training data period means a period of recording of NN learning data (input value/output value); a training data start time means an NN learning data collection start time point and a training data end time means an NN learning data collection end time point; the UE records an input value and an output value measured in units of training data periods between the training data start time and the training data end time; the feedback format is an example of transmitting information for learning the NN, and may mean a set of data for learning the NN through the input value/output value),
wherein the training data is generated based on a reference signal transmitted from the base station apparatus (JE, ¶¶ [0164]-[0165]: a channel quality may be acquired by measuring a received signal; hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal to noise ratio (SNR), signal to interference and noise ratio (SINR), carrier to interference and noise ratio (CINR), error vector magnitude (EVM), bit error rate (BER), block error rate (BLER), other terms having the technical meaning equivalent thereto, or indexes indicating a channel quality may be additionally used; ; ¶¶ [0170] and [0179]-[0201]: the UE receives handover-related reference information from the BS; the BS may transmit information related to the handover (hereinafter, referred to as handover information) to the UE in operation 620; the handover information may include NN input info: input value information of HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like); a feedback request is a value of which the BS informs in order to allow the UE to transmit learning information related to the handover for learning; the feedback format indicates a transmission format in which the UE feeds back handover learning data; the feedback format indicates which information is fed back for AI learning; NN input info is input value information of the handover NN used for the handover; e.g., the radio signal intensity such as RSRP, RSRQ, or SINR may be used as the input value; the input value may include the radio signal intensity and the throughput; when maximization of an RSRP value for a predetermined time period is aimed, the UE may add RSRP values of each BS for a predetermined time period after the handover and generate learning data to output a maximum BS(cell); ¶¶ [0391]-[0405]: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: NN input info: input value information of the HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like), throughput, an SRS, and a wireless signal which can be measured by the UE such as cqi and the like), and the training data is usable by the terminal device for handover by artificial intelligence and/or machine learning (JE, ¶¶ [0153]-[0154] and [0162]-[0164]: the AI-based handover procedure may include a scheme for transmitting the configured neural network model to the UE, a procedure for making a request for a handover to a target cell identified according to the configured model to a serving cell, or a learning procedure for updating the configured neural network mode; as a better quality of data is accumulated and construction of a neural network model which is a determination reference is more consistent with reality, a handover determination based on an AI algorithm may be more accurate for a situation that the UE or the BS individually faces; the neural network for the AI-based handover may require a plurality of input values; e.g., the neural network for the AI-based handover may consider a channel quality of the current serving cell, a channel quality of the target cell, and a type of the serving cell (e.g., whether the serving cell is a small cell or an RAT type) as an input; further, the neural network for the AI-based handover may provide a plurality of output values; e.g. the neural network for the AI-based handover may indicate a plurality of target cells to which the handover can be performed; an efficient handover may be achieved by applying an AI-based handover determination method to a wireless communication system between the BS and the UE through a combination of cell information and measurement information or environment information of the UE).
JE fails to explicitly disclose wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning.
LEE teaches a system and a method relating to machine learning in a wireless communication (LEE, ¶¶ [0002] and [0006]), wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning (LEE, ¶¶ [0100]-[0106]: the 6G system will support AI for full automation; advance in machine learning will create a more intelligent network for real-time communication in 6G; when AI is introduced to communication, real-time data transmission may be simplified and improve; AI may determine a method of performing complicated target tasks using countless analysis; i.e., AI may increase efficiency and reduce processing delay; time-consuming tasks such as handover, network selection or resource scheduling may be immediately performed by using AI; deep learning have been focused on the wireless resource management and allocation field; combine deep learning in the physical layer with wireless transmission are emerging; AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism; e.g., channel coding and decoding based on deep learning, signal estimation and detection based on deep learning, multiple input multiple output (MIMO) mechanisms based on deep learning, resource scheduling and allocation based on AI, etc. may be included; machine learning may be used for channel estimation and channel tracking and may be used for power allocation, interference cancellation, etc. in the physical layer of DL; in addition, machine learning may be used for antenna selection, power control, symbol detection, etc. in the MIMO system; deep learning-based AI algorithms require a lot of training data in order to optimize training parameters; however, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline; static training for training data in a specific channel environment may cause a contradiction between the diversity and dynamic characteristics of a radio channel; ¶¶ [0134]-[0144] with FIGS. 15A-C: it is not easy to collect a lot of data for training the neural network in a communication channel with a long-tail distribution; even if a terminal moves to experience a new channel, collects data and performs fine-tuning training for an offline-pretrained neural network again, the terminal can hardly reflect a new change sufficiently; neural network training should reflect effect of a channel between a transmitter and a receiver; a reference signal or a pilot signal may be used as training data to reflect a channel effect; e.g., a device may be subject to transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception; a neural network requirement technique is needed to enable a device to secure as many reference signal wireless resources as possible and to have fast learning in a propagation environment with a long-tail distribution; proposes a communication system, a communication procedure, and a signaling method, which are based on online meta learning; meta learning uses a neural network that is pretrained based on various tasks; meta learning is learning that uses such a pretrained neural network to enable a device to well perform inference like regression and classification for a new task; i.e., meta learning is a method of enhancing learning and estimation performance for a new task and is learning for learning (learn-to-learn); when a device experiences a new task, the device relearns a model parameter ϕ suitable for the new task from the meta parameter θ and performs inference based on the relearning; ¶¶ [0145]-[0149] with FIG. 16: proposes a meta learning method for a transmitting/receiving task associated with a reference signal; there are various reference signals; e.g., (a) a device may transmit a phase-tracking reference signal with a specific pattern in order to correct a phase error in a high-frequency band; (b) the device may transmit a demodulation reference signal (DM-RS) with a specific pattern in order to estimate a channel and to estimate data; (c) the device may transmit a channel state information-reference signal (CSI-RS) with a specific pattern in order to track time synchronization of a channel, to track a beam, and to find out channel quality information like a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI); (d) the device may transmit a sounding reference signal (SRS) with a specific pattern p for channel sounding; and (e) the terminal may transmit a positioning reference signal (PRS) for measuring a location; the device may transmit various types of reference signals according to purposes; define transmission/reception and measurement of reference signals as tasks so that a device may use a wireless resource as efficiently as possible; applying meta learning to transmission/reception and measurement of reference signals; the task may mean transmission/reception and measurement related to all reference signals that have passed a channel at time t, wherein the reference signals may include a CSI-RS, a PTRS, a PRS, a CRS, and a DMRS; in each task, a result of passing a channel may be used as a dataset; e.g., a terminal may perform a task of measuring a CQI based on a CSI-RS, wherein in case the terminal succeeds in CQI measurement based on the CSI-RS, a result may be used as one supervised learning dataset; online meta learning proposed may be divided mainly into two parts as follows: (a) meta-training is a procedure in which a device learns an optimal meta parameter θ based on a meta-training data set of every reference signal; and (b) adaptation is a procedure in which the device learns ϕ by learning based on the optimal meta parameter θ* to succeed in a task for a specific reference signal and performs a task related to the reference signal at the same time; in case the device succeeds in adaptation of the reference signal task, the device may obtain a reference signal dataset and a parameter ϕ for the task; when the device acquires θ*, meta training may be performed by inputting the reference signal dataset and the task parameter obtained from the adaptation; in addition, the device may acquire ϕ by performing a specific task based on θ* obtained from the meta training, thereby performing adaptation).
JE and LEE are analogous art because they are from the same field of endeavor, a system and a method relating to machine learning in a wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of LEE to JE. Motivation for doing so would easily expand AI techniques in a new task for increasing efficiency.
Independent Claim 3
JE discloses a communication method used in a terminal device (JE, ¶¶ [0017], [0050], and [0068] with 120 in FIG. 1: a user equipment (UE) or a mobile station (MS) in a wireless communication system),
wherein a first processing unit (JE, ¶¶ [0437] and [0444] with 1805 in FIG. 18: the UE 120 includes a controller 1805; the controller 1805 may include at least one processor or microprocessor or may be a part of the processor) that processes a signal for instructing start of training (JE, ¶¶ [0389]-[00391], [0050], and [0071] with 1620 in FIG. 16A: Learning for AI-Based Handover (HO); FIG. 16A illustrates an example of operations of the BS and the UE for learning; in operation 1620, the UE may receive feedback configuration information from the base station (BS); the feedback configuration information indicates a return of learning data through a feedback request value within handover indication information (HOinformationMessage); the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; ¶¶ [0409] and [0017] with 1660 in FIG. 16A: in operation 1660, the BS may learn a neural network (NN) model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0411] with 1670 in FIG. 16B: in operation 1670, the UE may receive feedback configuration information from the BS; ¶ [0413] with 16130 in FIG. 16B: when receiving a message including the learning data from the target BS, the source BS may learn the NN and the W for the AI HO in operation 16130; i.e., UE receives and processes a instructing signal from BS to collect training data for learning/training NN model) and a second processing unit (JE, ¶¶ [0424] and [0431]-[0433]: the BS 110 includes a controller 1707; the controller 1707 controls the overall operations of the BS 110; ¶¶ [0437] and [0444] with 1805 in FIG. 18: the UE 120 includes a controller 1805; the controller 1805 may include at least one processor or microprocessor or may be a part of the processor) that processes training data are provided (JE, ¶¶ [0391] and [0408]-[0409] with 1650 in FIG. 16A: the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; in operation 1650, the UE may feedback training data for the AI-based handover to the serving BS according to a format configured by the feedback configuration information; in operation 1660, the BS may learn a neural network model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0413] with 16100 and 16110 in FIG. 16B: the handover may occur while learning data is collected like in operation 1690, and thus the handover to the target BS is made; in operation 16110, the UE may transmit collected learning data to the target BS which is which is the current serving cell, not to the source BS in this case; when receiving the corresponding message, the target BS may identify an ID of the source BS (or source cell) within the message; since the ID is different from the ID of the target BS, the target BS may feedback learning data to another BS; in operation 16120, the target BS may transmit the learning data to the source BS),
the signal for instructing the start of the training includes a training start timing and a training period (JE, ¶¶ [0391]-[0405] with 1620 in FIG. 16A: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: (a) Neural Network (NN) input info; (b) NN output info; (c) Performance; (d) training data period: NN learning data collection period; (e) training data start time: NN learning data collection start time point; and (f) training data end time: NN learning data collection end time point), and
in a case where the signal for instructing the start of the training is received, the training data is generated based on the training start timing and the training period (JE, ¶¶ [0406]-[0407] with 1630 and 1640 in FIG. 16A: in operation 1630, the UE may collect training data on the basis of feedback configuration information; the UE records input info of the NN and outinfo of the NN in every training data period from a training data start time to a training data end time, and the output info of the NN configures the right answer based on performance; in operation 1640, the UE may detect expiration of the learning section; the BS may learn and update the structure information (NN) and the weight information (W) of the neural network model for the AI-based handover; ¶¶ [0412]-[0413] with 1680 and 16100 in FIG.16B: in operation 1680, the UE may acquire learning data on the basis of the feedback configuration information; the UE may detect expiration of the learning section in operation 16100; ¶ [0202]: a training data period means a period of recording of NN learning data (input value/output value); a training data start time means an NN learning data collection start time point and a training data end time means an NN learning data collection end time point; the UE records an input value and an output value measured in units of training data periods between the training data start time and the training data end time; the feedback format is an example of transmitting information for learning the NN, and may mean a set of data for learning the NN through the input value/output value),
wherein the training data is generated based on a reference signal transmitted from a base station apparatus (JE, ¶¶ [0164]-[0165]: a channel quality may be acquired by measuring a received signal; hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal to noise ratio (SNR), signal to interference and noise ratio (SINR), carrier to interference and noise ratio (CINR), error vector magnitude (EVM), bit error rate (BER), block error rate (BLER), other terms having the technical meaning equivalent thereto, or indexes indicating a channel quality may be additionally used; ; ¶¶ [0170] and [0179]-[0201]: the UE receives handover-related reference information from the BS; the BS may transmit information related to the handover (hereinafter, referred to as handover information) to the UE in operation 620; the handover information may include NN input info: input value information of HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like); a feedback request is a value of which the BS informs in order to allow the UE to transmit learning information related to the handover for learning; the feedback format indicates a transmission format in which the UE feeds back handover learning data; the feedback format indicates which information is fed back for AI learning; NN input info is input value information of the handover NN used for the handover; e.g., the radio signal intensity such as RSRP, RSRQ, or SINR may be used as the input value; the input value may include the radio signal intensity and the throughput; when maximization of an RSRP value for a predetermined time period is aimed, the UE may add RSRP values of each BS for a predetermined time period after the handover and generate learning data to output a maximum BS(cell); ¶¶ [0391]-[0405]: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: NN input info: input value information of the HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like), throughput, an SRS, and a wireless signal which can be measured by the UE such as cqi and the like), and the training data is usable by the terminal device for handover by artificial intelligence and/or machine learning (JE, ¶¶ [0153]-[0154] and [0162]-[0164]: the AI-based handover procedure may include a scheme for transmitting the configured neural network model to the UE, a procedure for making a request for a handover to a target cell identified according to the configured model to a serving cell, or a learning procedure for updating the configured neural network mode; as a better quality of data is accumulated and construction of a neural network model which is a determination reference is more consistent with reality, a handover determination based on an AI algorithm may be more accurate for a situation that the UE or the BS individually faces; the neural network for the AI-based handover may require a plurality of input values; e.g., the neural network for the AI-based handover may consider a channel quality of the current serving cell, a channel quality of the target cell, and a type of the serving cell (e.g., whether the serving cell is a small cell or an RAT type) as an input; further, the neural network for the AI-based handover may provide a plurality of output values; e.g. the neural network for the AI-based handover may indicate a plurality of target cells to which the handover can be performed; an efficient handover may be achieved by applying an AI-based handover determination method to a wireless communication system between the BS and the UE through a combination of cell information and measurement information or environment information of the UE).
LEE teaches a system and a method relating to machine learning in a wireless communication (LEE, ¶¶ [0002] and [0006]), wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning (LEE, ¶¶ [0100]-[0106]: the 6G system will support AI for full automation; advance in machine learning will create a more intelligent network for real-time communication in 6G; when AI is introduced to communication, real-time data transmission may be simplified and improve; AI may determine a method of performing complicated target tasks using countless analysis; i.e., AI may increase efficiency and reduce processing delay; time-consuming tasks such as handover, network selection or resource scheduling may be immediately performed by using AI; deep learning have been focused on the wireless resource management and allocation field; combine deep learning in the physical layer with wireless transmission are emerging; AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism; e.g., channel coding and decoding based on deep learning, signal estimation and detection based on deep learning, multiple input multiple output (MIMO) mechanisms based on deep learning, resource scheduling and allocation based on AI, etc. may be included; machine learning may be used for channel estimation and channel tracking and may be used for power allocation, interference cancellation, etc. in the physical layer of DL; in addition, machine learning may be used for antenna selection, power control, symbol detection, etc. in the MIMO system; deep learning-based AI algorithms require a lot of training data in order to optimize training parameters; however, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline; static training for training data in a specific channel environment may cause a contradiction between the diversity and dynamic characteristics of a radio channel; ¶¶ [0134]-[0144] with FIGS. 15A-C: it is not easy to collect a lot of data for training the neural network in a communication channel with a long-tail distribution; even if a terminal moves to experience a new channel, collects data and performs fine-tuning training for an offline-pretrained neural network again, the terminal can hardly reflect a new change sufficiently; neural network training should reflect effect of a channel between a transmitter and a receiver; a reference signal or a pilot signal may be used as training data to reflect a channel effect; e.g., a device may be subject to transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception; a neural network requirement technique is needed to enable a device to secure as many reference signal wireless resources as possible and to have fast learning in a propagation environment with a long-tail distribution; proposes a communication system, a communication procedure, and a signaling method, which are based on online meta learning; meta learning uses a neural network that is pretrained based on various tasks; meta learning is learning that uses such a pretrained neural network to enable a device to well perform inference like regression and classification for a new task; i.e., meta learning is a method of enhancing learning and estimation performance for a new task and is learning for learning (learn-to-learn); when a device experiences a new task, the device relearns a model parameter ϕ suitable for the new task from the meta parameter θ and performs inference based on the relearning; ¶¶ [0145]-[0149] with FIG. 16: proposes a meta learning method for a transmitting/receiving task associated with a reference signal; there are various reference signals; e.g., (a) a device may transmit a phase-tracking reference signal with a specific pattern in order to correct a phase error in a high-frequency band; (b) the device may transmit a demodulation reference signal (DM-RS) with a specific pattern in order to estimate a channel and to estimate data; (c) the device may transmit a channel state information-reference signal (CSI-RS) with a specific pattern in order to track time synchronization of a channel, to track a beam, and to find out channel quality information like a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI); (d) the device may transmit a sounding reference signal (SRS) with a specific pattern p for channel sounding; and (e) the terminal may transmit a positioning reference signal (PRS) for measuring a location; the device may transmit various types of reference signals according to purposes; define transmission/reception and measurement of reference signals as tasks so that a device may use a wireless resource as efficiently as possible; applying meta learning to transmission/reception and measurement of reference signals; the task may mean transmission/reception and measurement related to all reference signals that have passed a channel at time t, wherein the reference signals may include a CSI-RS, a PTRS, a PRS, a CRS, and a DMRS; in each task, a result of passing a channel may be used as a dataset; e.g., a terminal may perform a task of measuring a CQI based on a CSI-RS, wherein in case the terminal succeeds in CQI measurement based on the CSI-RS, a result may be used as one supervised learning dataset; online meta learning proposed may be divided mainly into two parts as follows: (a) meta-training is a procedure in which a device learns an optimal meta parameter θ based on a meta-training data set of every reference signal; and (b) adaptation is a procedure in which the device learns ϕ by learning based on the optimal meta parameter θ* to succeed in a task for a specific reference signal and performs a task related to the reference signal at the same time; in case the device succeeds in adaptation of the reference signal task, the device may obtain a reference signal dataset and a parameter ϕ for the task; when the device acquires θ*, meta training may be performed by inputting the reference signal dataset and the task parameter obtained from the adaptation; in addition, the device may acquire ϕ by performing a specific task based on θ* obtained from the meta training, thereby performing adaptation).
JE and LEE are analogous art because they are from the same field of endeavor, a system and a method relating to machine learning in a wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of LEE to JE. Motivation for doing so would easily expand AI techniques in a new task for increasing efficiency.
Independent Claim 4
JE discloses a communication method used in a base station apparatus (JE, ¶¶ [0016], [0050], and [0067] with 110 in FIG. 1: a base station (BS) of a serving cell in a wireless communication system),
wherein a first processing unit (JE, ¶¶ [0437] and [0444] with 1805 in FIG. 18: the UE 120 includes a controller 1805; the controller 1805 may include at least one processor or microprocessor or may be a part of the processor) that processes a signal for instructing start of training (JE, ¶¶ [0389]-[00391], [0050], and [0071] with 1620 in FIG. 16A: Learning for AI-Based Handover (HO); FIG. 16A illustrates an example of operations of the BS and the UE for learning; in operation 1620, the UE may receive feedback configuration information from the base station (BS); the feedback configuration information indicates a return of learning data through a feedback request value within handover indication information (HOinformationMessage); the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; ¶¶ [0409] and [0017] with 1660 in FIG. 16A: in operation 1660, the BS may learn a neural network (NN) model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0411] with 1670 in FIG. 16B: in operation 1670, the UE may receive feedback configuration information from the BS; ¶ [0413] with 16130 in FIG. 16B: when receiving a message including the learning data from the target BS, the source BS may learn the NN and the W for the AI HO in operation 16130; i.e., UE receives and processes a instructing signal from BS to collect training data for learning/training NN model) and a second processing unit (JE, ¶¶ [0424] and [0431]-[0433]: the BS 110 includes a controller 1707; the controller 1707 controls the overall operations of the BS 110; ¶¶ [0437] and [0444] with 1805 in FIG. 18: the UE 120 includes a controller 1805; the controller 1805 may include at least one processor or microprocessor or may be a part of the processor) that processes training data are provided (JE, ¶¶ [0391] and [0408]-[0409] with 1650 in FIG. 16A: the UE may acquire learning data according to the feedback configuration information and feedback the same to the BS; In operation 1650, the UE may feedback training data for the AI-based handover to the serving BS according to a format configured by the feedback configuration information; in operation 1660, the BS may learn a neural network model for the AI-based handover on the basis of the learning data acquired from the UE; ¶ [0413] with 16100 and 16110 in FIG. 16B: the handover may occur while learning data is collected like in operation 1690, and thus the handover to the target BS is made; in operation 16110, the UE may transmit collected learning data to the target BS which is which is the current serving cell, not to the source BS in this case; when receiving the corresponding message, the target BS may identify an ID of the source BS (or source cell) within the message; since the ID is different from the ID of the target BS, the target BS may feedback learning data to another BS; in operation 16120, the target BS may transmit the learning data to the source BS),
the signal for instructing the start of the training includes a training start timing and a training period (JE, ¶¶ [0391]-[0405] with 1620 in FIG. 16A: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: (a) Neural Network (NN) input info; (b) NN output info; (c) Performance; (d) training data period: NN learning data collection period; (e) training data start time: NN learning data collection start time point; and (f) training data end time: NN learning data collection end time point), and
in a case where the signal for instructing the start of the training is transmitted, the training data is instructed to be generated based on the training start timing and the training period (JE, ¶¶ [0406]-[0407] with 1630 and 1640 in FIG. 16A: in operation 1630, the UE may collect training data on the basis of feedback configuration information; the UE records input info of the NN and outinfo of the NN in every training data period from a training data start time to a training data end time, and the output info of the NN configures the right answer based on performance; in operation 1640, the UE may detect expiration of the learning section; the BS may learn and update the structure information (NN) and the weight information (W) of the neural network model for the AI-based handover; ¶¶ [0412]-[0413] with 1680 and 16100 in FIG.16B: in operation 1680, the UE may acquire learning data on the basis of the feedback configuration information; the UE may detect expiration of the learning section in operation 16100; ¶ [0202]: a training data period means a period of recording of NN learning data (input value/output value); a training data start time means an NN learning data collection start time point and a training data end time means an NN learning data collection end time point; the UE records an input value and an output value measured in units of training data periods between the training data start time and the training data end time; the feedback format is an example of transmitting information for learning the NN, and may mean a set of data for learning the NN through the input value/output value),
wherein the training data is generated based on a reference signal transmitted from a base station apparatus (JE, ¶¶ [0164]-[0165]: a channel quality may be acquired by measuring a received signal; hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal to noise ratio (SNR), signal to interference and noise ratio (SINR), carrier to interference and noise ratio (CINR), error vector magnitude (EVM), bit error rate (BER), block error rate (BLER), other terms having the technical meaning equivalent thereto, or indexes indicating a channel quality may be additionally used; ; ¶¶ [0170] and [0179]-[0201]: the UE receives handover-related reference information from the BS; the BS may transmit information related to the handover (hereinafter, referred to as handover information) to the UE in operation 620; the handover information may include NN input info: input value information of HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like); a feedback request is a value of which the BS informs in order to allow the UE to transmit learning information related to the handover for learning; the feedback format indicates a transmission format in which the UE feeds back handover learning data; the feedback format indicates which information is fed back for AI learning; NN input info is input value information of the handover NN used for the handover; e.g., the radio signal intensity such as RSRP, RSRQ, or SINR may be used as the input value; the input value may include the radio signal intensity and the throughput; when maximization of an RSRP value for a predetermined time period is aimed, the UE may add RSRP values of each BS for a predetermined time period after the handover and generate learning data to output a maximum BS(cell); ¶¶ [0391]-[0405]: the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information: NN input info: input value information of the HO NN, e.g., wireless signal intensity (RSRP, RSRQ, SINR, or the like), throughput, an SRS, and a wireless signal which can be measured by the UE such as cqi and the like), and the training data is usable by the terminal device for handover by artificial intelligence and/or machine learning (JE, ¶¶ [0153]-[0154] and [0162]-[0164]: the AI-based handover procedure may include a scheme for transmitting the configured neural network model to the UE, a procedure for making a request for a handover to a target cell identified according to the configured model to a serving cell, or a learning procedure for updating the configured neural network mode; as a better quality of data is accumulated and construction of a neural network model which is a determination reference is more consistent with reality, a handover determination based on an AI algorithm may be more accurate for a situation that the UE or the BS individually faces; the neural network for the AI-based handover may require a plurality of input values; e.g., the neural network for the AI-based handover may consider a channel quality of the current serving cell, a channel quality of the target cell, and a type of the serving cell (e.g., whether the serving cell is a small cell or an RAT type) as an input; further, the neural network for the AI-based handover may provide a plurality of output values; e.g. the neural network for the AI-based handover may indicate a plurality of target cells to which the handover can be performed; an efficient handover may be achieved by applying an AI-based handover determination method to a wireless communication system between the BS and the UE through a combination of cell information and measurement information or environment information of the UE).
LEE teaches a system and a method relating to machine learning in a wireless communication (LEE, ¶¶ [0002] and [0006]), wherein the training data is usable by the terminal device for channel estimation by artificial intelligence and/or machine learning (LEE, ¶¶ [0100]-[0106]: the 6G system will support AI for full automation; advance in machine learning will create a more intelligent network for real-time communication in 6G; when AI is introduced to communication, real-time data transmission may be simplified and improve; AI may determine a method of performing complicated target tasks using countless analysis; i.e., AI may increase efficiency and reduce processing delay; time-consuming tasks such as handover, network selection or resource scheduling may be immediately performed by using AI; deep learning have been focused on the wireless resource management and allocation field; combine deep learning in the physical layer with wireless transmission are emerging; AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism; e.g., channel coding and decoding based on deep learning, signal estimation and detection based on deep learning, multiple input multiple output (MIMO) mechanisms based on deep learning, resource scheduling and allocation based on AI, etc. may be included; machine learning may be used for channel estimation and channel tracking and may be used for power allocation, interference cancellation, etc. in the physical layer of DL; in addition, machine learning may be used for antenna selection, power control, symbol detection, etc. in the MIMO system; deep learning-based AI algorithms require a lot of training data in order to optimize training parameters; however, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline; static training for training data in a specific channel environment may cause a contradiction between the diversity and dynamic characteristics of a radio channel; ¶¶ [0134]-[0144] with FIGS. 15A-C: it is not easy to collect a lot of data for training the neural network in a communication channel with a long-tail distribution; even if a terminal moves to experience a new channel, collects data and performs fine-tuning training for an offline-pretrained neural network again, the terminal can hardly reflect a new change sufficiently; neural network training should reflect effect of a channel between a transmitter and a receiver; a reference signal or a pilot signal may be used as training data to reflect a channel effect; e.g., a device may be subject to transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception; a neural network requirement technique is needed to enable a device to secure as many reference signal wireless resources as possible and to have fast learning in a propagation environment with a long-tail distribution; proposes a communication system, a communication procedure, and a signaling method, which are based on online meta learning; meta learning uses a neural network that is pretrained based on various tasks; meta learning is learning that uses such a pretrained neural network to enable a device to well perform inference like regression and classification for a new task; i.e., meta learning is a method of enhancing learning and estimation performance for a new task and is learning for learning (learn-to-learn); when a device experiences a new task, the device relearns a model parameter ϕ suitable for the new task from the meta parameter θ and performs inference based on the relearning; ¶¶ [0145]-[0149] with FIG. 16: proposes a meta learning method for a transmitting/receiving task associated with a reference signal; there are various reference signals; e.g., (a) a device may transmit a phase-tracking reference signal with a specific pattern in order to correct a phase error in a high-frequency band; (b) the device may transmit a demodulation reference signal (DM-RS) with a specific pattern in order to estimate a channel and to estimate data; (c) the device may transmit a channel state information-reference signal (CSI-RS) with a specific pattern in order to track time synchronization of a channel, to track a beam, and to find out channel quality information like a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI); (d) the device may transmit a sounding reference signal (SRS) with a specific pattern p for channel sounding; and (e) the terminal may transmit a positioning reference signal (PRS) for measuring a location; the device may transmit various types of reference signals according to purposes; define transmission/reception and measurement of reference signals as tasks so that a device may use a wireless resource as efficiently as possible; applying meta learning to transmission/reception and measurement of reference signals; the task may mean transmission/reception and measurement related to all reference signals that have passed a channel at time t, wherein the reference signals may include a CSI-RS, a PTRS, a PRS, a CRS, and a DMRS; in each task, a result of passing a channel may be used as a dataset; e.g., a terminal may perform a task of measuring a CQI based on a CSI-RS, wherein in case the terminal succeeds in CQI measurement based on the CSI-RS, a result may be used as one supervised learning dataset; online meta learning proposed may be divided mainly into two parts as follows: (a) meta-training is a procedure in which a device learns an optimal meta parameter θ based on a meta-training data set of every reference signal; and (b) adaptation is a procedure in which the device learns ϕ by learning based on the optimal meta parameter θ* to succeed in a task for a specific reference signal and performs a task related to the reference signal at the same time; in case the device succeeds in adaptation of the reference signal task, the device may obtain a reference signal dataset and a parameter ϕ for the task; when the device acquires θ*, meta training may be performed by inputting the reference signal dataset and the task parameter obtained from the adaptation; in addition, the device may acquire ϕ by performing a specific task based on θ* obtained from the meta training, thereby performing adaptation).
JE and LEE are analogous art because they are from the same field of endeavor, a system and a method relating to machine learning in a wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to apply the teaching of LEE to JE. Motivation for doing so would easily expand AI techniques in a new task for increasing .
Claims 5-8
JE in view of LEE discloses all the elements as stated in Claims 1-4 respectively and further discloses wherein the reference signal includes any of a demodulation reference signal (DMRS), a phase tracking reference signal (PTRS), a channel state information reference signal (CSI-RS), or synchronization signal block (SSB) (LEE, ¶ [0136]: a reference signal or a pilot signal may be used as training data to reflect a channel effect; e.g., a device may be subject to transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception; a neural network requirement technique is needed to enable a device to secure as many reference signal wireless resources as possible and to have fast learning in a propagation environment with a long-tail distribution; ¶¶ [0145]-[0149] with FIG. 16: proposes a meta learning method for a transmitting/receiving task associated with a reference signal; there are various reference signals; e.g., (a) a device may transmit a phase-tracking reference signal with a specific pattern in order to correct a phase error in a high-frequency band; (b) the device may transmit a demodulation reference signal (DM-RS) with a specific pattern in order to estimate a channel and to estimate data; (c) the device may transmit a channel state information-reference signal (CSI-RS) with a specific pattern in order to track time synchronization of a channel, to track a beam, and to find out channel quality information like a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI); (d) the device may transmit a sounding reference signal (SRS) with a specific pattern p for channel sounding; and (e) the terminal may transmit a positioning reference signal (PRS) for measuring a location; the device may transmit various types of reference signals according to purposes; define transmission/reception and measurement of reference signals as tasks so that a device may use a wireless resource as efficiently as possible; applying meta learning to transmission/reception and measurement of reference signals; the task may mean transmission/reception and measurement related to all reference signals that have passed a channel at time t, wherein the reference signals may include a CSI-RS, a PTRS, a PRS, a CRS, and a DMRS; in each task, a result of passing a channel may be used as a dataset; e.g., a terminal may perform a task of measuring a CQI based on a CSI-RS, wherein in case the terminal succeeds in CQI measurement based on the CSI-RS, a result may be used as one supervised learning dataset; ¶ [0153]: a preferred RS set is a set consisting of every possible combination of RSs for meta learning; e.g., the preferred RS set may include at least one of a DMRS, a PTRS, a CSI-RS, a PRS, a synchronization signal block (SSB), an SRS, a cell-specific reference signal (CRS), and a meta learning RS).
Response to Arguments
Applicant’s arguments filed on 12/28/2025 with respect to Claims 1-4 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HAO et al. (US 2025/0088232 A1, filed on 04/02/2022) discloses in ABSTRACT and ¶¶ [0005]-[0012] that (1) provide techniques for channel estimation based on transmission spatial information; (2) receiving an indication of transmission spatial information associated with a network entity; (3) receiving a reference signal from the network entity based on the transmission spatial information; and (4) transmitting, to the network entity, channel state information (CSI) based on the received reference signal and the transmission spatial information. HAO further discloses in ¶¶ [0037]-[0044] that (1) wireless communication networks (e.g., 5G New Radio (NR) systems or other wireless systems) may use channel state information (CSI) feedback from a user equipment (UE) for adaptive communications; (2) a network entity (e.g., a base station) may adjust certain communication parameters at a UE in response to CSI feedback from the UE; e.g., link adaptation (such as adaptive modulation and coding and/or power control) with various modulation schemes and channel coding rates may be applied to certain communication channels; (3) for channel state estimation purposes, the UE may be configured to measure a reference signal (e.g., a CSI reference signal (CSI-RS)) and estimate the downlink channel state based on the CSI-RS measurements; (4) the UE may report an estimated channel state to the network in the form of CSI, which may be used in link adaptation; (5) the CSI may indicate channel properties of a communication link between a BS and a UE; (6) the CSI may represent the effect of, e.g., scattering, fading, and pathloss of a signal across the communication link; (6) 5G NR systems may support a network entity with antenna architectures having up to 32 antenna ports; (7) under such a configuration, the UE may perform channel estimation on up to 32 antenna ports occupying the same number of resource elements; (8) the channel estimation processing complexity at a UE may be proportional to the frequency bandwidth associated with the ports; (9) as the number of ports increase, the UE may be tasked to process an increasing frequency bandwidth associated with the ports for channel estimation and/or CSI reporting; (10) in certain cases, the UE may not have the capability to process the corresponding frequency bandwidth associated with a large number of ports and corresponding antenna elements associated with a network entity; (11) using transmission spatial information associated with channel estimation and/or reporting CSI, wherein the transmission spatial information may indicate a mapping of TxRUs to antenna elements of an antenna architecture; (12) a network entity may send (for example, to a UE) an indication of the transmission spatial information associated with a certain network entity, and the UE may use the transmission spatial information to determine a channel estimation and/or CSI; (13) the UE may use machine learning to determine the channel estimation and/or CSI based on the transmission spatial information; (14) facilitate CS I-RS overhead reduction with usage of a machine learning module to perform channel estimation and/or determine CSI and reduce the CSI-RS overhead; (15) the transmission spatial information may be used to train, select, and/or configure the machine learning module; (16) the transmission spatial information may facilitate machine learning at the UE, e.g., due to a reduced frequency resource occupation (e.g., the number of resource elements L) associated with the number of CSI-RS ports represented by the transmission spatial information; (17) the transmission spatial information may facilitate flexible configurations at the network and/or UE, such as a flexible TxRU to antenna! Element mapping configuration at the network entity and/or a flexible machine learning configuration at the UE; e.g., the transmission spatial information may allow for unique antenna architectures at the network entity, where the transmission spatial information indicates a standardized mapping for TxRUs; and (18) the transmission spatial information may allow for unique machine learning models at the UE, where the transmission spatial may be used to train, select, and/or configure the machine learning model. HAO further discloses in ¶¶ [0072]-[0081] with FIG. 5 that (1) the UE 104 may monitor for downlink reference signals from the BS 102, such as a CSI-RS and/or SSB associated with beam(s) 504; (2) the UE 104 may determine a channel estimation 506 and/or CSI 508 associated with the beams 504 based at least in part on the received reference signals corresponding to the beams 504 and the transmission spatial information 502; (3) the UE 104 may report the CSI 508 to the BS 102; (4) in some cases, the UE 104 may use the channel estimation 506 for transmit and/or receive beamforming; (5) in certain cases, the BS 102 may transmit the reference signals (e.g., CSI-RS and/or SSB) using beams or via cover-codes, which may be obtained by machine learning and/or artificial intelligence (AI/ML) at the BS 102 and/or UE 104; (6) the CSI-RS may be cross-node operation for AI/ML at the BS 102 and the UE 104; (7) under a cross-node operation, a beam or cover-code obtained by AI/ML may be used by the BS 102 to transmit the CSI-RS; (8) the UE 104 may employ an AI/ML based channel estimation, where the AI/ML module at the BS 102 and the UE 104 may be matched, e.g., jointly trained; (9) similarly, CSI feedback may also be cross-node for AI/ML at the BS 102 and the UE 104; (10) the UE 104 may use a CSI encoder to compress the channel estimate to a small dimension and report the CSI to the BS 102; (11) the BS 102 may employ a CSI decoder to recover the full channel; (12) in certain cases, the CSI encoder and decoder are matched AI/ML modules, e.g., jointly trained AI/ML modules; (13) in certain cases, the UE 104 may perform artificial intelligence ( e.g., a neural network and/or machine learning) and/or regression analysis (e.g., a linear minimum mean square error (LMMSE) operation) to determine the channel estimation 506 and/or the CSI 508; e.g., the UE 104 may use a machine learning module 510 to determine the channel estimation 506 and/or the CSI 508; (14) the input 512 of the machine learning module 510 may include measurements of the received reference signal(s) (e.g., a CSI-RS and/or SSB) from the BS 102; e.g. the input 512 may include the received reference signal represented in the frequency domain; (15) the output 514 of the machine learning module 510 may include the channel estimation 506 and/or the CSI 508; (16) the machine learning module 510 may output the channel estimation 506 associated with each port of an antenna architecture in the frequency domain; (17) the machine learning module 510 may be trained in various ways; (18) the network entity ( e.g., the BS 102) may provide the UE 104 with training data to train the machine learning module 510; (19) the network entity (e.g., the BS 102) may transmit a certain reference signal to train the machine learning module 510 at the UE 104; (20) the machine learning module 510 may be pre-trained to process the channel estimation 506 and/or the CSI 508; (21) the transmission spatial information 502 may indicate a mapping 516 of TxRUs 522 (e.g., CSI-RS ports) to antenna elements 524 in an antenna architecture across a certain frequency bandwidth (e.g., a number or resource elements per resource block); (22) the transmission spatial information 502 may indicate one or more cover-codes 526 (X) in the mapping 516, wherein each of the cover-codes 526 may be applied to a reference signal 528 (s) per resource element among a certain number of resource elements (REO through RE L-1) per TxRU 522 (e.g., CSI-RS port P); (23) the cover-codes 526 may indicate the beamforming applied to the reference signals, such as the amplitude and/or phase control, for a port; e.g., the cover-codes associated with a resource block (RB) may provide that P ports map to L resource elements (RE) per resource block, where the number of resource elements L is less than the number of ports P; (24) the UE may perform a channel estimation, which estimates the channel between the UE and the BS on P ports across N resource blocks, where the number of resource blocks N may be greater than or equal to the number resource blocks where CSI-RS is transmitted (N~_CSI-RS), using the input of CSI-RSs with a size reflected as LxN_CSI-RS; (25) in a data-driven operation (e.g., artificial intelligence or machine learning), a machine learning module (e.g., the machine learning module 510) and/or a neural network may be used to determine the channel estimation, and another machine learning module or neural network may be used to determine the cover-code; and (26) the two machine learning modules and/or neural networks may be matched or trained jointly, wherein the machine learning module may be trained to adapt to the spatial correlation (e.g., the transmission spatial information 502) of the training channel samples. HAO also discloses in ¶¶ [0110]-[0119] that (1) the transmission spatial information may be indicated via a quasi-colocation (QCL) information, such as a QCL type and/or a QCL reference Signal; (2) the QCL reference signal for transmission spatial information may include an SSB, a TRS, CSI-RS, a separate RS associated with conveying the transmission spatial information and/or synthesis training data; (3) the target signal and/or channel may be considered quasi collocated with a specific TRS/CSI-RS or synthesis training data associated with the transmission spatial information; and (4) the UE may perform channel estimation based on the QCL information for transmission spatial information. HAO further teaches in ¶¶ [0132]-[] with FIG. 16 that (1) at activity 1602, the UE 104 may receive, from the BS 102, an indication of transmission spatial information; (2) at activity 1604, the UE 104 may receive, from the BS 102, one or more reference signals associated with the transmission spatial information; (3) at activity 1606, the UE 104 may train or select a machine learning module based on the transmission spatial information; (4) at activity 1608, the UE 104 may perform channel estimation of the channel between the UE 104 and the BS 102 based on the transmission spatial information and measurements of the reference signal(s); (5) at activity 1610, the UE 104 may determine CSI based on the channel estimation and/or the transmission spatial information; (6) at activity 1612, the UE 104 may transmit, to the BS 102, the CSI based on the transmission spatial information and/or received reference signal(s); e.g., the CSI may indicate a CQI, a PMI, a RSRP, and/or SINR associated with the reference signal(s); (7) at activity 1614, the UE 104 may communicate with the BS 102 based on the CSL; and (8) the model may be used to determine the CSI/channel estimation associated with reference signals based on the transmission spatial information (e.g., a cover-code configuration) indicated by a network entity; (9) the model may be trained based on training data (e.g., training information), which may include feedback, such as feedback associated with the CSI/channel estimation (e.g., measurements of reference signals).
ZHANG et al. (US 2024/0020542 A1, filed on 12/16/2021) discloses in ¶¶ [0003]-[0034] that (1) in new radio (NR), there may be two types of reference signals in UL, e.g., DMRS (Demodulation Reference Signal) and SRS (Sounding Reference Signal), which may give information about channel quality; (2) the base station may estimate the channel quality using the reference signal and perform resource scheduling, beam management, and power control of signal; (3) the existing reference signals may not be suitable for air-interface machine learning; (4) online supervised machine learning may train itself based on RS; (5) for end to end AI (artificial intelligence) inference network, it may conduct channel estimation, equalization, decoding in one processing; (6) online training may avoid inaccuracy introduced by possible manually defined modeling error and can handle more realistic scenarios efficiently and automatically; (7) training data may be expected to be bit/symbol sequences, which is not supported in a current communication system such as NR in terms of easy enabling and controlling of flexible and efficiency training data transmission, collections and training; e.g. the current reference signals such as DMRS, SRS, PT-RS (Phase Tracking-Reference signal), CSI-RS (Channel State Information Reference Signal) are all designed for channel state information (CSI) estimation for either channel estimation, channel equalization, or phase correction, channel quality without including coding/decoding effect; (8) the control information for training data may comprise at least one of physical resources information for the training data, power control information for the training data, modulation type information for the training data, coding method information for the training data, mapping method information of the training data to physical resources, multi-antenna related information for the training data, or an instruction regarding how to generate a bit/symbol sequence of the training data; and (9) the physical resources information may comprise at least one of time resource allocation information, or frequency resource allocation information.
SABER et al. (US 2023/0131694 A1, priority date on 10/19/2021) discloses in ABSTRACT and ¶¶ [0005]-[0007] that (1) receive a signal using a channel; (2) transmit a representation of channel information relating to the channel; (3) determine a condition of the channel based on the signal; (4) generate the representation of the channel information based on the condition of the channel using a machine learning model; (5) the channel information may include a channel estimation and precoding information; (6) perform a selection of the machine learning model based on the condition of the channel; (7) activate the machine learning model based on model identification information received using the receiver; (8) receive the model identification information using one or more of a media access control (MAC) signal or a radio resource control (RRC) signal; (9) indicate the selection of the machine learning model using the transmitter; (10) receive the machine learning model; (11) receive a quantization function corresponding to the machine learning model; (12) train the machine learning model using a quantization function, wherein the quantization function may include a differentiable quantization function and an approximated quantization function; (13) send configuration information for the machine learning model, wherein the configuration information may include one or more or a weight or a hyperparameter; (14) the machine learning model may be a generation model, and train the generation model using a reconstruction model that may be configured to reconstruct the channel information based on the representation; (15) the generation model may include an encoder, and the reconstruction model may include a decoder; (16) receive configuration information for the reconstruction model, and train the generation model based on the configuration information, wherein the configuration information may include one or more or a weight or a hyperparameter; (17) perform joint training of the generation model and the reconstruction model; (18) send the reconstruction model based on the joint training; (19) collect training data for the machine learning model based on the channel; (20) collect the training data based on a resource window, wherein the resource window has a time dimension and a frequency dimension; (21) the channel information may include a channel matrix, a singular value matrix combined with a singular value, and a unitary matrix; (22) preprocess the channel information to generate transformed channel information, and generate the representation of the channel information based on the transformed channel information; (23) preprocess the channel information based on a transformation, and train the machine learning model based on training data, wherein the training data may be processed based on the transformation; (24) process the training data based on the transformation; (25) train the machine learning model using a processing allowance; (26) the processing allowance may include a processing time; (27) the processing allowance may be initiated based on the signal; (28) the processing allowance may be initiated based on a control signal; (29) the control signal may include one or more of a media access control (MAC) signal or a radio resource control (RRC) signal; (30) send the representation of the channel information as link control information; (31) send the link control information as uplink control information (UCI); (32) quantize the representation of the channel information to generate a quantized representation; (33) apply a coding scheme to the quantized representation to generate a coded representation, wherein the coding scheme may include a polar coding scheme, and send the coded representation using a physical control channel; and (34) the coding scheme may include a low-density parity-check (LDPC) coding scheme, and send the coded representation using a physical shared channel; (35) determining physical layer information for the wireless apparatus; (36) generating a representation of the physical layer information using a machine learning model; (37) transmitting the representation of the physical layer information; (38) the machine learning model may be a generation mode; (39) training the generation model using a reconstruction model that may be configured to reconstruct the physical layer information based on the representation; (40) collecting training data for the machine learning model based on a resource window; (41) the physical layer information may include a channel matrix; (42) preprocessing the physical layer information to generate transformed physical layer information, and generating the representation of the physical layer information based on the transformed physical layer information; (43) the generating may be performed based on a processing allowance; (44) activating the machine learning model based on model identification information received at the wireless apparatus; and (45) the representation of the physical layer information may include uplink control information. SABER further discloses in ¶¶ [0024]-[0048] that (1) in a 5G New Radio (NR) system, a base station (e.g., a gNodeB or gNB) may send a reference signal to a user equipment (UE) through a downlink (DL) channel; (2) the UE may measure the reference signal to determine channel conditions on the DL channel; (3) the UE may then send feedback information (e.g., channel state information (CSI)) indicating the channel conditions on the DL channel to the base station through an uplink (UL) channel; (4) the base station may use the feedback information to improve the manner in which it transmits to the UE through the DL channel, e.g., through the use of beamforming; (5) sending feedback information on channel conditions, however, may consume a relatively large amount of resources as overhead; (6) to reduce the amount of data used to transmit feedback information, use one or more types of codebooks to enable a receiving device to send implicit and/or explicit channel condition feedback to a transmitting device; (7) a feedback scheme may use artificial intelligence (AI), machine learning (ML), deep learning, and/or the like (any or all of which may be referred to individually and/or collectively as machine learning or ML) to generate a representation of physical layer information for a wireless communication system; e.g., a feedback scheme may use an ML model to generate a representation of feedback information for a channel condition ( e.g., a representation of a channel matrix, a precoding matrix, and/or the like); (8) a model that generates a representation of an input (e.g., physical layer information such as feedback information for a channel condition) may be referred to as a generation model; (9) a model that reconstructs an input, or an approximation of the input, from a representation of the input may be referred to as a reconstruction model; (10) a generation model and a corresponding reconstruction model may be referred to collectively as a pair of ML models or a pair of models; (12) a generation model may be implemented as an encoder model, and/or a reconstruction model may be implemented as a decoder model; (13) training data may be collected based on a resource window (e.g., a window of time and/or frequency resources); e.g., a node may be configured to collect training data (e.g., channel estimates) for a specific range of frequencies (e.g., subcarriers, sub-bands, etc.) and a specific range of times (e.g., symbols, slots, etc.); (14) the size of a window may be determined, e.g., based on an amount of training data a node may be able to store in memory; (15) the collected training data may be used for online training by one or more nodes or saved for offline training; (16) perform online training of a model (e.g., using a training data set that is provided to the node or collected by the node), the node may be expected to update the model within a predetermined number of symbols or other measure of time' and (16) CSI compression performance may be improved using AI and/or ML, e.g., by exploiting one or more correlations in the time, frequency and/or space domains, and/or by defining a training data set across time, frequency, and/or space. SABER also discloses in ¶¶ [0049]-[0058] with FIGS. 1-2 that (1) the representation 107 of the physical layer information may be a compressed, encoded, encrypted, mapped, or otherwise modified form of the physical layer information 105; (2) depending on the implementation details, the modification of the physical layer information 105 by the machine learning model 103 to generate the representation 107 of the physical layer information may reduce the resources involved in transmitting the physical layer information 105 between apparatus; (3) the physical layer information 105 may include any information relating to the operation of a physical layer of a wireless communication apparatus; e.g., the physical layer information 105 may include information (e.g., status information, precoding information, etc.) relating to one or more physical layer channels, signals, beams, and/or the like; and (4) examples of physical layer channels may include one or more of a physical broadcast channel (PBCH), physical random access channel (PRACH), physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), physical uplink shared channel (PUSCH), physical uplink control channel (PUCCH), physical sidelink shared channel (PSSCH), physical sidelink control channel (PSCCH), physical sidelink feedback channel (PSFCH), and/or the like; (5) examples of physical layer signals may include one or more of a primary synchronization signal (PSS), secondary synchronization signal (SSS), channel state information reference signal (CSI-RS), tracking reference signal (TRS), sounding reference signal (SRS), and/or the like. SABER further teaches in ¶¶ [0059]-[0065] with FIG. 3 that (1) training data 311 may be applied to a generation model 303 which may generate a representation 307 of the training data; (2) a reconstruction model 304 may generate a reconstruction 312 of the training data based on the representation 307 of the training data; (3) the generation model 303 and reconstruction model 304 may be trained as a pair, e.g., by using a loss function 313 to provide training feedback 314 to the generation model 303 and/or the reconstruction model 304; (4) the loss function 313 (which may be implemented, e.g., at least partially with a reconstruction loss) may operate to train the generation model 303 and reconstruction model 304 to generate the reconstructed training data 312 to be close to the original training data 311; (5) the pair of models 303 and 304 may seek to reduce the dimensionality of the representation 307 of the training data relative to the original training data 311; and (6) once trained, the generation model 303 and/or reconstruction model 304 may be used for inference. SABER also teaches in ¶¶ [0104]-[0124] that (1) a node may declare or be assigned a predetermined memory buffer capability based on (a) a time gap (e.g., a maximum time gap) for obtaining training data and/or updating a model based on the obtained training data; (b) a maximum number of reference signals (e.g., CSI-RSs) within a time window the node is expected to use for constructing a training set; or and (c) the shorted periodicity of reference signals (e.g., CSIRSs) used for constructing the training data set; (2) a situation in which a node may be configured with one or more reference signals and/or a time window that may violate the predetermined memory buffer capability may be considered an error case; (3) alternatively, or additionally, a default behavior may be defined when a violation of the predetermined memory buffer capability of a node occurs; e.g., if a configuration of reference signals and/or a time window violates a node's memory buffer capacity, the node may use only store and/or use a subset of the collected training data to update a model; (4) a buffer size for collected training data may be based on a node implementation, e.g., without involving a specification; e.g., if a UE's training data buffer overflows, the UE may stop storing newly collected data (e.g., matrices) and proceed to update the model with the data in the buffer; and (5) UE may use a shared buffer to store new training data., wherein examples of shared buffers may include one or more buffers already used for storing other channels, e.g., a PDSCH buffer, a master CCE LLR buffer, and/or the like.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HWEI-MIN LU/Primary Examiner, Art Unit 2142