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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/11/24 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C.119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0057953 and KR10-2023-0031409, filed on 5/11/2022 and 3/9/2023 respectively.
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.
Claim(s) 1-5, 8-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. US 20250330856 (hereinafter “Yang”) in view of Bai et al. US 20200259545 (hereinafter “Bai”)
As to claim 1 and 11 (claim 1 is the method claim for the base station in claim 11):
Yang discloses:
A method performed by a base station in a wireless communication system, the method comprising: obtaining, from a terminal, channel information including current channel state information, based on a channel state information (CSI) report in a first time interval; (“For example, a base station may receive CSI feedback from a UE and a may determine precoder values for precoders corresponding to the UE based on the CSI feedback, for example through signal to leakage ratio in some implementation. The base station may utilize the determined precoder values for the precoders for precoding signals to be transmitted to the UE.”, Yang [0058])
identifying, based on the channel information, (“The base station may determine the precoders for a layer for a UE based on CSI received from the UE. For example, precoders for a layer may be given by size-P×N.sub.3 matrix”, Yang [0060]) a spatial domain (SD) component (“P may be equal to 2N.sub.1N.sub.2, which may be equal to a number of spatial domain (SD) dimensions”, Yang [0060]), a frequency domain (FD) component (“where W.sub.1 is a spatial beam selection or a spatial beam basis selection, {tilde over (W)}.sub.2 is a bitmap design and quantizer design for connecting spatial beams and FD components, and is a FD component basis selection”, Yang [0060]), and a linear combination (LC) coefficient value mapped to the SD component and the FD component having been used for compression; (“For example, the user equipment (UE) can report selected spatial beams per Doppler component, can report selected frequency domain (FD) components per Doppler component, and/or can report selected time domain (TD) components. Further, the UE can report any or all of the number of selected spatial beams, the number of selected FD components, and/or the number of selected of TD components. The non-zero (NZ) linear coefficient (LC) coefficients' selection may be through a bitmap and component composition patterns. Alternatively, the NZ LC coefficients' selection may be through multiple bitmaps, or the Doppler offset may be predicted at least from the spatial beam, the delay offset, and/or the UE position.”, Yang [0042]) (“Some embodiments describe adaptations of image processing/video processing technology to facilitate the CSI compression and feedback. Aspects describe various approaches for CSI feedback with machine learning, time domain compression for high-speed wireless channels, and machine-learning-aided feedback.”, Yang [0052])
Yang as described above does not explicitly teach:
obtaining a filtered LC coefficient value, based on a Kalman filter; and generating predicted channel information in a second time interval, based on the SD component, the FD component, and the filtered LC coefficient value.
However, Bai further teaches generating predicted channel information which includes:
obtaining a filtered LC coefficient value, based on a Kalman filter; and generating predicted channel information in a second time interval, based on the SD component, the FD component, and the filtered LC coefficient value. (“the prediction algorithm 405 may use one or more filters, for example, such as a Kalman filter, which may use a linear combination of past measurements (e.g., past channel quality measurements) to determine a future measurement (e.g., a future value of a channel quality parameter). The examples of prediction algorithms described herein are a non-exhaustive list, and other prediction algorithms may be supported by the base station 105 and the UE 115. In some examples, the selection and usage of the prediction algorithm 405 may be defined (e.g., by a network operator) per base station or per UE. The base station 105 and the UE 115 may, in some examples, select and use a same or different prediction algorithm to determine (e.g., predict) a future quality of a communication link (e.g., of an active beam pair).”, Bai [0134])(“The prediction algorithm 405 may predict a future value of a channel quality parameter based in part on one or more measurements 410 or side information 415, or both. The one or more measurements 410 may include a value of a channel quality parameter (e.g., RSRP, RSRQ, SNR, SINR) measured by a base station 105 or a UE 115, or both. The side information may include UE mobility information, a doppler spread, previous beam switching events, or the like. Thus, the prediction algorithm 405 may output one or more predicted values 420 (e.g., a future value), such as a future value of a channel quality parameter.”, Bai [0135])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
As to claim 2 and 12 (claim 2 is the method claim for the base station in claim 12):
Yang discloses:
The method of claim 1, further comprising: obtaining a rotation matrix, based on previous channel state information and a correlation between the SD component and the FD component; (“The base station may determine the precoders for a layer for a UE based on CSI received from the UE. For example, precoders for a layer may be given by size-P×N.sub.3 matrix where W.sub.1 is a spatial beam selection or a spatial beam basis selection, {tilde over (W)}.sub.2 is a bitmap design and quantizer design for connecting spatial beams and FD components, and is a FD component basis selection. P may be equal to 2N.sub.1N.sub.2, which may be equal to a number of spatial domain (SD) dimensions, N.sub.1 is the number of antenna ports in one dimension (e.g. for the vertical domain, and N.sub.1=2 for FIG. 2) and N.sub.2 is the number of antenna ports in another dimension (e.g. for the horizon domain, and N.sub.2=4 for FIG. 2). N.sub.3 may be equal to a number of FD dimensions. Precoder normalization may be applied, where the precoder normalization may be defined by the precoding matrix for given rank and unit of N.sub.3 is normalized to norm 1/sqrt(rank), where sqrt(rank) is the square root of a rank indicator.”, Yang [0060]) (“The spatial beam selection representation 200 may include a number of groups (e.g., two groups) in a first direction and a number of groups (e.g., four groups) in a second direction, resulting in a matrix of a groups, e.g., two by four arrangement of groups. Each group may have a number of spatial beams, e.g., four spatial beams in the first direction and a number of spatial beams, e.g., four spatial beams in the second direction. For example, the spatial beam selection representation 200 may include a first group 202. The first group 202 may include 16 spatial beams in a four by four arrangement. The first group 202 may include an orthogonal discrete Fourier transform (DFT) beam 204, as indicated by the filled in circle in the spatial beam selection representation 200. The first group 202 may include a rotated DFT beam 206, as indicated by the circle with diagonal lines in the spatial beam selection representation 200. The rotated DFT beam 206 may have rotation factors of…”, Yang [0065])
obtaining a filtered previous LC coefficient value, based on the (“The spatial beam selection 1000 may provide beam selection for a spatial layer or multiple spatial layers (e.g. all the spatial layers), such as indicated by a spatial beam selection representation 1031, 1032, 1033 and 1034. In 1000, 4 spatial beams are selected commonly for two antenna polarizations, which results into a common spatial beam selection for all sheets. After the common spatial beam selection, then a sheet-specific spatial beam selection is conducted. Alternatively, without going through a common spatial beam stage, the spatial beam selection for each sheet can be conducted independently, including choosing different rotation factors. As such, the spatial beam selection representation 1002 for a given sheet may include one or more of the features of the spatial beam selection representation 200 (FIG. 2). The spatial beam selection representation 1002 may include orthogonal DFT beams as indicated by the black filled circles, rotated DF beams as indicated by the diagonal stripe filled circles, and/or oversampled DFT beams as indicated by the unfilled circles. The rotated DFT beams in the illustrated embodiment may be rotated from the orthogonal DFT beams by rotation factors of “, Yang [0116])(“The UE may further filter the sheets to remove sheets that do not include non-zero LC coefficients. The UE may indicate sheets that do not include non-zero LC coefficients that have been filtered, or may not indicate the sheets that do not include non-zero LC coefficients that have been filtered in a CSI report to a base station. For example, filtered sheet representations 1120 in the illustrated embodiment may have filtered the fourth sheet 1108 based on the fourth sheet not including any non-zero LC coefficients. Accordingly, the filtered sheet representation 1120 may include the first sheet 1102, the second sheet 1104, the third sheet 1106, and the fifth sheet 1110 in the illustrated representation. For each of non-zero location on the bitmap (represented by the sheets in the illustrated embodiment), there is at least one LC coefficient at all the selected sheets for the filtered sheet representation 1120. Filtering of the sheets that do not include non-zero LC coefficients may reduce the size of signals (such as CSI reports) transmitted by the UE to the base station, which may reduce overhead.”, Yang [0133])
Yang as described above does not explicitly teach:
and generating the predicted channel information in the second time interval, based on the filtered previous LC coefficient value and the filtered LC coefficient value.
However, Bai further teaches generating predicted channel information which includes:
and generating the predicted channel information in the second time interval, based on the filtered previous LC coefficient value and the filtered LC coefficient value. (“In other examples, the learning algorithm may use one or more filters, for example, such as a Kalman filter, which may use a linear combination of past measurements (e.g., a past value of a channel quality parameter) to determine a future measurement (e.g., a future value of the channel quality parameter). The examples of learning algorithms described herein are a non-exhaustive list, and other learning algorithms may be supported by the base station 105-a and the UE 115-a. In some examples, the selection and usage of a learning algorithm may be defined (e.g., by a network operator) per base station or per UE. The base station 105-a and the UE 115-a may, in some examples, select and use a same or different learning algorithm to determine (e.g., predict) a future value of a channel quality parameter of a communication link (e.g., of an active beam pair) between the base station 105-a and the UE 115-a.”, Bai [0112])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
As to claim 3 and 13 (claim 3 is the method claim for the base station in claim 13):
Yang as described above does not explicitly teach:
The method of claim 2, wherein the Kalman filter comprises at least one of a linear Kalman filter (LKF), an enhanced Kalman filter (EKF), or an unscented Kalman filter (UKF).
However, Bai further teaches generating predicted channel information which includes:
The method of claim 2, wherein the Kalman filter comprises at least one of a linear Kalman filter (LKF), an enhanced Kalman filter (EKF), or an unscented Kalman filter (UKF). (“In other examples, the learning algorithm may use one or more filters, for example, such as a Kalman filter, which may use a linear combination of past measurements (e.g., a past value of a channel quality parameter) to determine a future measurement (e.g., a future value of the channel quality parameter). The examples of learning algorithms described herein are a non-exhaustive list, and other learning algorithms may be supported by the base station 105-a and the UE 115-a. In some examples, the selection and usage of a learning algorithm may be defined (e.g., by a network operator) per base station or per UE. The base station 105-a and the UE 115-a may, in some examples, select and use a same or different learning algorithm to determine (e.g., predict) a future value of a channel quality parameter of a communication link (e.g., of an active beam pair) between the base station 105-a and the UE 115-a.”, Bai [0112])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
As to claim 4 and 14 (claim 4 is the method claim for the base station in claim 14):
Yang as described above does not explicitly teach:
The method of claim 2, wherein the previous channel state information comprises information on channel parameters before the first time interval, and wherein the current channel state information comprises information on channel parameters in the first time interval.
However, Bai further teaches generating predicted channel information which includes:
The method of claim 2, wherein the previous channel state information comprises information on channel parameters before the first time interval, and wherein the current channel state information comprises information on channel parameters in the first time interval. (“the prediction algorithm 405 may use one or more filters, for example, such as a Kalman filter, which may use a linear combination of past measurements (e.g., past channel quality measurements) to determine a future measurement (e.g., a future value of a channel quality parameter). The examples of prediction algorithms described herein are a non-exhaustive list, and other prediction algorithms may be supported by the base station 105 and the UE 115. In some examples, the selection and usage of the prediction algorithm 405 may be defined (e.g., by a network operator) per base station or per UE. The base station 105 and the UE 115 may, in some examples, select and use a same or different prediction algorithm to determine (e.g., predict) a future quality of a communication link (e.g., of an active beam pair).”, Bai [0134])(“The prediction algorithm 405 may predict a future value of a channel quality parameter based in part on one or more measurements 410 or side information 415, or both. The one or more measurements 410 may include a value of a channel quality parameter (e.g., RSRP, RSRQ, SNR, SINR) measured by a base station 105 or a UE 115, or both. The side information may include UE mobility information, a doppler spread, previous beam switching events, or the like. Thus, the prediction algorithm 405 may output one or more predicted values 420 (e.g., a future value), such as a future value of a channel quality parameter.”, Bai [0135])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
As to claim 5 and 15 (claim 5 is the method claim for the base station in claim 15):
Yang discloses:
The method of claim 4, wherein the channel parameters comprise at least one of a Doppler parameter, a delay parameter, or a spatial vector according to an antenna. (“The UE may report the Doppler component composition signaling with the spatial beam selection representation and/or the FD component selection representation from the single sheet representation 1116 in a CSI report in some embodiments.”, Yang [0135])
As to claim 8 and 18 (claim 8 is the method claim for the base station in claim 18):
Yang as described above does not explicitly teach:
The method of claim 1, wherein the generating of the predicted channel information comprises: obtaining a time delay parameter and a Doppler parameter of the current channel state information; and generating the predicted channel information, based on the time delay parameter, the Doppler parameter, and resource difference information.
However, Bai further teaches generating predicted channel information which includes:
The method of claim 1, wherein the generating of the predicted channel information comprises: obtaining a time delay parameter and a Doppler parameter of the current channel state information; and generating the predicted channel information, based on the time delay parameter, the Doppler parameter, and resource difference information. (“the prediction algorithm 405 may use one or more filters, for example, such as a Kalman filter, which may use a linear combination of past measurements (e.g., past channel quality measurements) to determine a future measurement (e.g., a future value of a channel quality parameter). The examples of prediction algorithms described herein are a non-exhaustive list, and other prediction algorithms may be supported by the base station 105 and the UE 115. In some examples, the selection and usage of the prediction algorithm 405 may be defined (e.g., by a network operator) per base station or per UE. The base station 105 and the UE 115 may, in some examples, select and use a same or different prediction algorithm to determine (e.g., predict) a future quality of a communication link (e.g., of an active beam pair).”, Bai [0134])(“The prediction algorithm 405 may predict a future value of a channel quality parameter based in part on one or more measurements 410 or side information 415, or both. The one or more measurements 410 may include a value of a channel quality parameter (e.g., RSRP, RSRQ, SNR, SINR) measured by a base station 105 or a UE 115, or both. The side information may include UE mobility information, a doppler spread, previous beam switching events, or the like. Thus, the prediction algorithm 405 may output one or more predicted values 420 (e.g., a future value), such as a future value of a channel quality parameter.”, Bai [0135])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
As to claim 9 and 19 (claim 9 is the method claim for the base station in claim 19):
Yang discloses:
The method of claim 1, wherein the SD component corresponds to a matrix related to a spatial beam, (“The base station may determine the precoders for a layer for a UE based on CSI received from the UE. For example, precoders for a layer may be given by size-P×N.sub.3 matrix”, Yang [0060]) (“where W.sub.1 is a spatial beam selection or a spatial beam basis selection, {tilde over (W)}.sub.2 is a bitmap design and quantizer design for connecting spatial beams and FD components, and”, Yang [0060]) (“is a FD component basis selection. P may be equal to 2N.sub.1N.sub.2, which may be equal to a number of spatial domain (SD) dimensions, N.sub.1 is the number of antenna ports in one dimension (e.g. for the vertical domain, and N.sub.1=2 for FIG. 2) and N.sub.2 is the number of antenna ports in another dimension (e.g. for the horizon domain, and N.sub.2=4 for FIG. 2). N.sub.3 may be equal to a number of FD dimensions. Precoder normalization may be applied, where the precoder normalization may be defined by the precoding matrix for given rank and unit of N.sub.3 is normalized to norm 1/sqrt(rank), where sqrt(rank) is the square root of a rank indicator”, Yang [0060])
wherein the FD component corresponds to a matrix related to a discrete Fourier transform (DFT) vector in a frequency domain, (“are M size-N.sub.3×1 orthogonal DFT vectors to select FD components with significant power for a spatial layer. Number of FD-components M may be configurable. L and M may be configured by gNB. In some embodiments, the FD compression unit may be determined by the number of CQI subbands and {PMI subband size=CQI subband size} as the default, and may be determined by {PMI subband size=CQI subband size/R} as a secondary choice. The value of R may be fixed to two. The FD compression unit parameter R may be higher-layered configured. The number of FD compression units, M, may be determined by”, Yang [0062])
and wherein the LC coefficient value corresponds to a matrix related to a beam angle and time-delay sparsity. (“Embodiments disclosed herein may introduce the time-domain in the codebook design. For example, the user equipment (UE) can report selected spatial beams per Doppler component, can report selected frequency domain (FD) components per Doppler component, and/or can report selected time domain (TD) components. Further, the UE can report any or all of the number of selected spatial beams, the number of selected FD components, and/or the number of selected of TD components. The non-zero (NZ) linear coefficient (LC) coefficients' selection may be through a bitmap and component composition patterns. Alternatively, the NZ LC coefficients' selection may be through multiple bitmaps, or the Doppler offset may be predicted at least from the spatial beam, the delay offset, and/or the UE position.”, Yang [0042]) (“The UE may perform phase quantization with the LC coefficients with the second polarization 506. In particular, the UE may perform phase quantization to indicate the phase of the LC coefficients in the second polarization 506. In some embodiments, the phase quantization may be performed with four bits. For example, the UE may perform phase quantization with the LC coefficients with the first polarization 504 to 16PSK in some embodiments. The phase may be based on an FD component of the strongest LC in the polarization. In the illustrated embodiments, the strongest LC for the second polarization 506 may be the tenth coefficient 526, which is in the fourth FD component of the modified bitmap 532. The UE may perform the phase quantization with the phase from the tenth coefficient 526 where C.sub.{circumflex over (b)},p may be a strongest LC coefficient, (f.sub.b,p−f.sub.{circumflex over (b)},p) may be a (relative) frequency offset, and (τ.sub.b,p−τ.sub.{circumflex over (b)},p) may be a relative delay. τ.sub.{circumflex over (b)},p may be a time delay of the strongest LC coefficient and may be utilized to shift the LC coefficients. The τ.sub.{circumflex over (b)},p may be used to shift the strongest LC coefficient to the first FD component (or the first tap).”, Yang [0091])
As to claim 10 and 20 (claim 10 is the method claim for the base station in claim 20):
Yang as described above does not explicitly teach:
The method of claim 1, wherein the obtaining of the channel information comprises: transmitting a CSI-reference signal (RS) to the terminal; and receiving CSI including a precoding matrix indicator (PMI) from the terminal, based on the CSI-RS, wherein the CSI-RS is periodically transmitted according to period T, and wherein the second time interval corresponds to a time interval before a time interval corresponding to period T after the first time interval.
However, Bai further teaches generating predicted channel information which includes:
The method of claim 1, wherein the obtaining of the channel information comprises: transmitting a CSI-reference signal (RS) to the terminal; (“A base station (e.g., eNodeB (eNB), a next-generation NodeB or giga-NodeB (either of which may be referred to as a gNB)) and a user equipment (UE) may perform a beam management procedure such as a beam switch procedure or a beam failure recovery procedure. In an example beam switch procedure, a base station may configure one or more reference signals. A subset of the configured reference signals may be used by the UE to monitor downlink and uplink control channels or data channels, while another subset may be used to identify candidate beams (e.g., satisfying a threshold). For example, a UE may monitor a reference signal, for example, a channel state information reference signal (C SI-RS).”, Bai [0059])
and receiving CSI including a precoding matrix indicator (PMI) from the terminal, based on the CSI-RS, (“The UE may measure a signal quality of the configured reference signals (e.g., based on the CSI-RS) and may transmit a report of the signal quality to the base station. The signal quality may be indicated by a signal to noise ratio (SNR), a reference signal received power (RSRP), a channel quality indicator (CQI), a rank indicator (RI), or a precoding matrix indicator (PMI), etc.”, Bai [0059])
wherein the CSI-RS is periodically transmitted according to period T, (“By way of example, the base station 105-a may transmit a reference signal periodically or aperiodically to the UE 115-a, so that the UE 115-a may perform channel measurements to monitor a value of a channel quality parameter of the communication link between the base station 105-a and the UE 115-a.”, Bai [0108])
and wherein the second time interval corresponds to a time interval before a time interval corresponding to period T after the first time interval. (“In some examples, the UE 115-a may identify a time stamp for the determined actual value of the channel quality parameter, where the side information or the report may indicate the identified time stamp. The UE 115-a may include a time stamp of the determined actual value of the channel quality parameter because, in some examples, the UE 115-a may be offered multiple chances to measure an active beam before given a chance to report the measurement results to the base station 105-a. That is, a periodicity of the base station 105-a transmitting one or more reference signals, and that of reporting occurrences by the UE 115-a may be different. Thus, to predict a future value of a channel quality parameter (e.g., RSRP), the timing of the measurement matters for the base station 105-a. In some examples, the UE 115-a may be configured to report a single value of a channel quality parameter (e.g., a single RSRP for a beam pair link). If the UE 115-a measures the value of the channel quality parameter (e.g., RSRP) multiple times before a reporting occurrence, the UE 115-a may determine how to report or which measurement value to report to the base station 105-a. For example, the UE 115-a may determine an average of the measured values of the channel quality parameter or choose to report a maximum or minimum of the measured values of the channel quality parameter. The base station 105-a may configure the UE 115-a to report all measurements of an active beam in a sequential manner, based in part on the base station 105-a ability to associate the measurement correctly with the reported measurement value.”, Bai [0121]) (“By way of example, the base station 105 (and the UE 115) may use a learning algorithm to determine a future quality of a communication link (e.g., of an active beam pair) between the base station 105 and the UE 115. The learning algorithm may be a deep learning algorithm, for example, such as a deep neural network algorithm (e.g., unsupervised pre-trained neural networks, convolutional neural networks, recurrent neural networks, recursive neural networks, or the like). The base station 105-a (and the UE 115) may determine (e.g., predict, forecast, estimate) a future value of a channel quality parameter (e.g., RSRP) based in part on previous and/or present uplink reference signal measurements performed by the base station 105, previous and/or present downlink reference signal measurements performed by the UE 115, side information provided by the UE 115, or a combination thereof as described herein. For example, the base station 105 (and the UE 115) may determine at a time instance 325 that an active beam may fail at a future time (e.g., x slots, where x is a positive integer) based in part on a prediction algorithm. As illustrated in FIG. 3, the RSRP of the reference signal associated with the active beam may fall below the RSRP threshold 350. Thus, the base station 105 (and the UE 115) may proactively switch active beams in advance to reduce a beam failure event and having to perform a beam failure recovery procedure. For example, the base station 105 (and the UE 115) may transmit a beam switch message that may include beam switch timing information (e.g., time and frequency resources, symbol index, etc.) and a request for the UE 115 (and the base station 105) to perform a beam switch procedure, during a beam switch window”, Bai [0130]) (“FIG. 5 illustrates an example of a process flow 500 that supports beam management using channel state information prediction in accordance with aspects of the present disclosure. The process flow 500 may implement aspects of the wireless communications systems 100 and 200, such as providing improvements in beam management procedures. The process flow 500 may include a base station 105-b and a UE 115-b, which may be examples of the corresponding devices described with reference to FIGS. 1 and 2. In the following description of the process flow 500, the operations between the base station 105-b and the UE 115-b may be transmitted in a different order than the exemplary order shown, or the operations performed by the base station 105-b and the UE 115-b may be performed in different orders or at different times. Certain operations may also be omitted from the process flow 500, and/or other operations may be added to the process flow 500.”, Bai [0136])
Yang and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating predicted channel information as described in Bai into Yang. By modifying the method to include generating predicted channel information as taught by Bai, the benefits of reduced overhead signaling (Yang [0043]) and decreased communications latency (Bai [0007]) are achieved.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Bai, as applied to claim 1 above, and further in view of Jeon et al. US 20190097694 (hereinafter “Jeon”)
As to claim 6 and 16 (claim 6 is the method claim for the base station in claim 16):
The combination of Yang and Bai as described above does not explicitly teach:
The method of claim 2, further comprising storing information on the rotation matrix, based on a lookup table (LUT).
However, Jeon further teaches storing information on a rotation matrix which includes:
The method of claim 2, further comprising storing information on the rotation matrix, based on a lookup table (LUT). (“The rotation matrix generator 330 may generate the rotation matrix F by using the singular values σ1 and σ2 and a lookup table LUT stored in the memory MEM. In some embodiments, the rotation matrix generator 330 may calculate the ratio of the singular value σ1 to the singular value σ2. The ‘γ’ corresponding to the ratio of the singular value σ1 to the singular value σ2 may be defined as the following Equation (6).”, Jeon [0053])
Yang, Jeon, and Bai are analogous because they pertain to using channel state information.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include storing information on a rotation matrix as described in Jeon into Yang as modified by Bai. By modifying the method to include storing information on a rotation matrix as taught by Jeon, the benefits of reduced overhead signaling (Yang [0043]), improved signal demodulation (Jeon [0027]), and decreased communications latency (Bai [0007]) are achieved.
Allowable Subject Matter
Claim 7 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
As per claim 7 and 17 the cited prior art either alone or in combination fails to teach the combined features of:
The method of claim 2, further comprising: identifying a moving speed of the terminal; determining whether a set of the SD component and the FD component is a changing set or a non-changing set; and based on a result of the determining and the moving speed of the terminal, determining whether to obtain the rotation matrix.
The base station of claim 12, wherein the controller is further configured to: identify a moving speed of the terminal, determine whether a set of the SD component and the FD component is a changing set or a non-changing set, and based on a result of the determining and the moving speed of the terminal, determine whether to obtain the rotation matrix.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW C KIM whose telephone number is (703)756-5607. The examiner can normally be reached M-F 9AM - 5PM (PST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy K Kundu can be reached at (571) 272-8586. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.C.K./
Examiner
Art Unit 2471
/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471
Prosecution Insights