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
The following is a non-final office action in response to applicant’s amendment filed on 05/18/2026 for response of the office action mailed on 02/27/2026. Claims 1-30 are pending in this application.
Response to Arguments
Applicant’s arguments with respect to Claims 1-30 have been fully considered and are persuasive. Therefore, the previous rejection has been withdrawn. Examiner agrees that the current application was obtained directly or indirectly from a joint inventor, and hence Hu does not qualify as prior art under 35. U.S.C. 102(b)(2)(A). However, upon further consideration, a new ground(s) of rejection is made. Therefore, applicant’s arguments with respect to Claims 1-30 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.
Claim Rejections - 35 USC § 103
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.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 8, 16 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over SNOPS (Tomasi, B., Decurninge, A., & Guillaud, M. (2016, September 7). SNOPS: Short Non-Orthogonal Pilot Sequences for Downlink Channel State Estimation in FDD Massive MIMO), SNOPS hereinafter, and further in view of Lee et al. (US 2022/0368570), Lee hereinafter.
Re. Claim 1,
SNOPS teaches a method for wireless communication at a user equipment (UE), comprising: receiving, from a base station, a reference signal (RS) on a set of resource elements (REs), the RS having been multiplexed onto the set of REs based on a non-orthogonal cover code, a number of REs in the set of REs being less than a number of ports associated with the RS; (Fig. 1 & Abstract - We introduce a scheme relying on non-orthogonal pilot sequences and feedback from the user terminal (UT), which enables the BTS to estimate all downlink channels. Thanks to the relaxed orthogonality assumption on the pilots, the length of the obtained pilot sequences can be strictly lower than the number of antennas at the BTS, while the CSI estimation error is kept arbitrarily small. Page 2, I Introduction: The BTS designs a set of non-orthogonal pilots based on statistical CSI, and transmits them … As will be seen, this enables to dynamically control the accuracy of CSI estimation, as well as to use short, non-orthogonal pilot sequences (SNOPS), suitably adapted to the channel statistics. Page 2-3, II. System Model, B: Let us assume that pilot sequences of overall length T are simultaneously broadcasted by all M antennas; we let matrix P∈CT×M denote the complete set of pilot sequences used in the system …The signal received at UT k over the corresponding T time instants … can be expressed as (Equation 2));
Yet, SNOPS does not explicitly teach estimating, at the UE via a channel estimation neural network, a channel based on receiving the RS; and transmitting, to the base station, a feedback report associated with the estimated channel based on receiving the RS.
However, in the analogous art, Lee explicitly teaches estimating, at the UE via a channel estimation neural network, a channel based on receiving the RS; and transmitting, to the base station, a feedback report associated with the estimated channel based on receiving the RS (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Re. Claim 8,
SNOPS teaches a method for wireless communication at base station, comprising: transmitting, to a user equipment (UE), the RS on the set of REs; multiplexing a reference signal (RS) onto a set of resource elements (REs) based on a non-orthogonal cover code, a number of REs in the set of REs being less than a number of antenna ports associated with the RS; (Fig. 1 & Abstract - We introduce a scheme relying on non-orthogonal pilot sequences and feedback from the user terminal (UT), which enables the BTS to estimate all downlink channels. Thanks to the relaxed orthogonality assumption on the pilots, the length of the obtained pilot sequences can be strictly lower than the number of antennas at the BTS, while the CSI estimation error is kept arbitrarily small. Page 2, I Introduction: The BTS designs a set of non-orthogonal pilots based on statistical CSI, and transmits them … As will be seen, this enables to dynamically control the accuracy of CSI estimation, as well as to use short, non-orthogonal pilot sequences (SNOPS), suitably adapted to the channel statistics. Page 2-3, II. System Model, B: Let us assume that pilot sequences of overall length T are simultaneously broadcasted by all M antennas; we let matrix P∈CT×M denote the complete set of pilot sequences used in the system …The signal received at UT k over the corresponding T time instants … can be expressed as (Equation 2));
Yet, SNOPS does not explicitly teach receiving, from the UE, a feedback report associated with the RS; and recovering, at the base station, an estimate of a channel associated with the RS based on receiving the feedback report.
However, in the analogous art, Lee explicitly teaches receiving, from the UE, a feedback report associated with the RS; and recovering, at the base station, an estimate of a channel associated with the RS based on receiving the feedback report (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Re. Claim 16,
SNOPS teaches an apparatus for wireless communications at a user equipment (UE), comprising: a processor; a memory coupled with the processor; and instructions stored in the memory and operable, when executed by the processor, to cause the apparatus to: receive, from a base station, a reference signal (RS) on a set of resource elements (REs), the RS having been multiplexed onto the set of REs based on a non-orthogonal cover code, a number of REs in the set of REs being less than a number of ports associated with the RS; (Fig. 1 & Abstract - We introduce a scheme relying on non-orthogonal pilot sequences and feedback from the user terminal (UT), which enables the BTS to estimate all downlink channels. Thanks to the relaxed orthogonality assumption on the pilots, the length of the obtained pilot sequences can be strictly lower than the number of antennas at the BTS, while the CSI estimation error is kept arbitrarily small. Page 2, I Introduction: The BTS designs a set of non-orthogonal pilots based on statistical CSI, and transmits them … As will be seen, this enables to dynamically control the accuracy of CSI estimation, as well as to use short, non-orthogonal pilot sequences (SNOPS), suitably adapted to the channel statistics. Page 2-3, II. System Model, B: Let us assume that pilot sequences of overall length T are simultaneously broadcasted by all M antennas; we let matrix P∈CT×M denote the complete set of pilot sequences used in the system …The signal received at UT k over the corresponding T time instants … can be expressed as (Equation 2));
Yet, SNOPS does not explicitly teach estimate, at the UE via a channel estimation neural network, a channel based on receiving the RS; and transmit, to the base station, a feedback report associated with the estimated channel based on receiving the RS.
However, in the analogous art, Lee explicitly teaches estimate, at the UE via a channel estimation neural network, a channel based on receiving the RS; and transmit, to the base station, a feedback report associated with the estimated channel based on receiving the RS (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Re. Claim 23,
SNOPS teaches an apparatus for wireless communications at base station, comprising: a processor; a memory coupled with the processor; and instructions stored in the memory and operable, when executed by the processor, to cause the apparatus to: transmit, to a user equipment (UE), the RS on the set of REs; multiplex a reference signal (RS) on onto a set of resource elements (REs) based on a non-orthogonal cover code, a number of REs in the set of REs being less than a number of antenna ports associated with the RS; (Fig. 1 & Abstract - We introduce a scheme relying on non-orthogonal pilot sequences and feedback from the user terminal (UT), which enables the BTS to estimate all downlink channels. Thanks to the relaxed orthogonality assumption on the pilots, the length of the obtained pilot sequences can be strictly lower than the number of antennas at the BTS, while the CSI estimation error is kept arbitrarily small. Page 2, I Introduction: The BTS designs a set of non-orthogonal pilots based on statistical CSI, and transmits them … As will be seen, this enables to dynamically control the accuracy of CSI estimation, as well as to use short, non-orthogonal pilot sequences (SNOPS), suitably adapted to the channel statistics. Page 2-3, II. System Model, B: Let us assume that pilot sequences of overall length T are simultaneously broadcasted by all M antennas; we let matrix P∈CT×M denote the complete set of pilot sequences used in the system …The signal received at UT k over the corresponding T time instants … can be expressed as (Equation 2));
Yet, SNOPS does not explicitly teach receive, from the UE, a feedback report associated with the RS; and recover, at the base station, an estimate of a channel associated with the RS based on receiving the feedback report.
However, in the analogous art, Lee explicitly teaches receive, from the UE, a feedback report associated with the RS; and recover, at the base station, an estimate of a channel associated with the RS based on receiving the feedback report (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Claims 5-6, 12-13, 15, 20-21, 27-28 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over SNOPS and Lee as applied to Claims 1, 8, 16 and 23 above, and further in view of Li et al. (US 2019/0326974), Li hereinafter.
Re. Claims 5 and 20, SNOPS and Lee teach Claims 1 and 16.
Yet, SNOPS does not explicitly teach the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook used by the channel estimation neural network.
However, in the analogous art, Lee explicitly teaches used by the channel estimation neural network (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Yet, SNOPS and Lee do not explicitly teach the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook
However, in the analogous art, Li explicitly teaches the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook (Fig. 1-5 & ¶0168 - In some possible implementations, before the receiving, by the base station, s first linear combination coefficient groups transmitted by the terminal device… ¶0172 - In some possible implementations, before the receiving, by the base station, second linear combination coefficients transmitted by the terminal device… ¶0235 - For example, the signals transmitted by the base station to the terminal device may be used to measure a channel coefficient of a channel from each port in the n port groups to the terminal device. ¶0282 - After receiving the s first linear combination coefficient groups reported by the terminal device, the base station may further determine a W.sub.2 codebook corresponding to the s first linear combination coefficient groups. Please also see ¶0259, ¶0284 and ¶0289);
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Re. Claims 6, 13, 21 and 28, SNOPS and Lee and Li teach Claims 5, 12, 20 and 27.
Yet, SNOPS and Lee do not explicitly teach the first group of channel coefficients are linear combination coefficients associated with variables of a diagonal block of a matrix associated with the codebook; and the second group of channel coefficients are linear combination coefficients associated with the matrix.
However, in the analogous art, Li explicitly teaches the first group of channel coefficients are linear combination coefficients associated with variables of a diagonal block of a matrix associated with the codebook; and the second group of channel coefficients are linear combination coefficients associated with the matrix (Fig. 1-5 & ¶0103 - where W.sub.3 is a matrix including the s first linear combination coefficient groups, W.sub.1 is a matrix including the at least two base vectors, and W.sub.2 is a matrix including the second linear combination coefficients. ¶0107 - In some possible implementations, W.sub.3 satisfies the following expression: Please see ¶0107-¶0114. ¶0282 - After receiving the s first linear combination coefficient groups reported by the terminal device, the base station may further determine a W.sub.2 codebook corresponding to the s first linear combination coefficient groups. ¶0241 - where W.sub.3 is a matrix including the s first linear combination coefficient groups, W.sub.1 is a matrix including the at least two base vectors, and W.sub.2 is a matrix including the second linear combination coefficients. ¶0283 - Optionally, a structure of the W.sub.2 codebook structure may be expressed as: Please see ¶00012).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Re. Claims 12 and 27, SNOPS and Lee and Li teach Claims 8 and 23.
Yet, SNOPS does not explicitly teach the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook used by a channel estimation neural network of the UE to estimate the channel.
However, in the analogous art, Lee explicitly teaches used by a channel estimation neural network of the UE to estimate the channel (Fig. 2 & ¶0040 - In order to provide a CSI report to the gNB, the UE may obtain or calculate a channel estimate based on received reference signals, for example … According to an example embodiment, ML-based estimation techniques (or neural network models) may be applied to the channel estimation problems by using the channel observation samples as input data … Or, at least in some cases, a channel estimation based on a neural network model may be more accurate in a situation where there are relatively few (or relatively infrequent) received reference signals or pilot signal by the UE … Other situations may exist in which more accurate channel estimation may be obtained by the UE via use of a trained neural network model. Fig. 3 & ¶0055 - Operation 330 includes determining, by the first user device, channel estimation information based at least on the portion of the pre-trained model. And, operation 340 includes performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Yet, SNOPS and Lee do not explicitly teach the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook
However, in the analogous art, Li explicitly teaches the feedback report indicates a first group of channel coefficients and a second group of channel coefficients associated with a codebook (Fig. 1-5 & ¶0168 - In some possible implementations, before the receiving, by the base station, s first linear combination coefficient groups transmitted by the terminal device… ¶0172 - In some possible implementations, before the receiving, by the base station, second linear combination coefficients transmitted by the terminal device… ¶0235 - For example, the signals transmitted by the base station to the terminal device may be used to measure a channel coefficient of a channel from each port in the n port groups to the terminal device. ¶0282 - After receiving the s first linear combination coefficient groups reported by the terminal device, the base station may further determine a W.sub.2 codebook corresponding to the s first linear combination coefficient groups. Please also see ¶0259, ¶0284 and ¶0289).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Re. Claim 15, SNOPS and Lee and Li teach Claim 13.
Yet, SNOPS and Lee do not explicitly teach reconstructing the matrix based on a basis pool and the first group of channel coefficients, wherein the estimate of the channel is recovered based on a product of the matrix and the second group of channel coefficients.
However, in the analogous art, Li explicitly teaches reconstructing the matrix based on a basis pool and the first group of channel coefficients, (Fig. 1-5 & ¶0012 - In some possible implementations, the first precoding matrix is obtained based on the s first linear combination coefficient groups and s base vector groups, and each base vector group includes base vectors of one of the s port groups);
wherein the estimate of the channel is recovered based on a product of the matrix and the second group of channel coefficients (¶0094 - transmitting, by the terminal device, s first linear combination coefficient groups, base vector information, and second linear combination coefficients, where the s first linear combination coefficient groups, the base vector information, and the second linear combination coefficients are determined based on measurement results of the reference signals of then port groups… ¶0259 - The new channel coefficient H′ is a product of the downlink channel matrix H and a new precoding matrix u, where u=a.sub.1*u.sub.1+ . . . +a_m.sub.0*u_m.sub.0. |H′|.sup.2 represents power of H in a spatial angle direction represented by u. In this embodiment of the present invention, linear combination coefficients (a.sub.1, . . . , a_m.sub.0) are selected properly, so that a spatial angle represented by u can exactly match a spatial angle in which the terminal device is located, and that the terminal device obtains a channel measurement result that better matches a real channel of the terminal device).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Re. Claim 30, SNOPS and Lee and Li teach Claim 28.
Yet, SNOPS and Lee do not explicitly teach execution of the instructions further cause the apparatus to reconstruct a matrix of the codebook based on a basis pool and the first group of channel coefficients, wherein the estimate of the channel is recovered based on a product of the matrix and the second group of channel coefficients.
However, in the analogous art, Li explicitly teaches execution of the instructions further cause the apparatus to reconstruct a matrix of the codebook based on a basis pool and the first group of channel coefficients, (Fig. 1-5 & ¶0012 - In some possible implementations, the first precoding matrix is obtained based on the s first linear combination coefficient groups and s base vector groups, and each base vector group includes base vectors of one of the s port groups);
wherein the estimate of the channel is recovered based on a product of the matrix and the second group of channel coefficients (¶0094 - transmitting, by the terminal device, s first linear combination coefficient groups, base vector information, and second linear combination coefficients, where the s first linear combination coefficient groups, the base vector information, and the second linear combination coefficients are determined based on measurement results of the reference signals of then port groups… ¶0259 - The new channel coefficient H′ is a product of the downlink channel matrix H and a new precoding matrix u, where u=a.sub.1*u.sub.1+ . . . +a_m.sub.0*u_m.sub.0. |H′|.sup.2 represents power of H in a spatial angle direction represented by u. In this embodiment of the present invention, linear combination coefficients (a.sub.1, . . . , a_m.sub.0) are selected properly, so that a spatial angle represented by u can exactly match a spatial angle in which the terminal device is located, and that the terminal device obtains a channel measurement result that better matches a real channel of the terminal device).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Claims 2-4, 9-11, 17-19, 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over SNOPS and Lee as applied to Claims 1, 8, 16 and 23 above, and further in view of He et al. (US 2024/0088953), He hereinafter.
Re. Claim 2, SNOPS and Lee teach Claim 1.
Yet, SNOPS does not explicitly teach further comprising quantizing one or more values associated with a measurement of the RS, wherein the feedback report indicates the one or more quantized values associated with the RS.
However, in the analogous art, Lee explicitly teaches further comprising quantizing one or more values associated with a measurement of the RS, (Fig. 2-3 & ¶0033 - As an illustrative example, a UE may measure one or more signal parameters (e.g., link quality) of reference signals received from a BS, and may send a channel state information (CSI) report to the BS. An example CSI report, may include, for example, one or more of: a RSRP (reference signal receive power); … a Precoder Matrix Indicator (PMI), which may indicate what a device (e.g., UE) estimates as a suitable precoder matrix based on the selected rank; and a Channel Quality Indication (or channel quality indicator) (CQI), which may express or indicate the BS-UE channel or link quality, as measured by the UE … For example, the gNB or BS may then adjust its UL transmission parameters (e.g., precoder, MCS, and/or a number of transmission layers, etc.) for transmitting to the UE, based on the received CSI report);
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Yet, SNOPS and Lee do not explicitly teach wherein the feedback report indicates the one or more quantized values associated with the RS;
However, in the analogous art, He explicitly teaches wherein the feedback report indicates the one or more quantized values associated with the RS (Fig. 1-5 & ¶0003 - In a new radio (NR) system, a beam training procedure between a base station and a terminal device is completed by using a channel state information (CSI) reporting procedure … The terminal device receives each reference signal configured by the base station, measures reference signal received power (RSRP) of the reference signal, and then reports reference signal indexes of several reference signals with relatively high RSRP and quantized RSRP values corresponding to the several reference signals. ¶0136 (Please see Fig. 3) - S305: The second device sends the channel state indication information to the first device. ¶0137 - S306: The first device receives the channel state indicator information reported by the second device).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Re. Claims 3, 10, 18 and 25, SNOPS and Lee and He teach Claims 2, 9, 17 and 24.
Yet, SNOPS and Lee do not explicitly teach the one or more quantized values include both a quantized amplitude of the measurement of the RS and a quantized phase of the measurement of the RS.
However, in the analogous art, He explicitly teaches the one or more quantized values include both a quantized amplitude of the measurement of the RS and a quantized phase of the measurement of the RS (Fig. 1-5 & ¶0130 - Based on the K received measurement values of the K received reference signals, the K first spatial filtering parameters, and the channel sparse basis matrix D, the second device may estimate a channel to obtain an estimation value ĥ of the channel. The estimation value of the channel is an estimation value obtained by quantizing an amplitude and a phase of an element in a matrix corresponding to the channel, and the estimation value ĥ of the channel is a vector including a plurality of elements. Please also see ¶0016).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Re. Claim 4, SNOPS and Lee and He teach Claim 2.
Yet, SNOPS and Lee do not explicitly teach a value of the measurement of the RS is a complex number; and the one or more quantized values include both a quantized real value of the measurement of the RS and a quantized imaginary value of the measurement of the RS.
However, in the analogous art, He explicitly teaches a value of the measurement of the RS is a complex number; and the one or more quantized values include both a quantized real value of the measurement of the RS and a quantized imaginary value of the measurement of the RS (Fig. 1-5 & ¶0097 - Each element in the analog precoding vector mk is a complex number, an amplitude of each element is 1/√{square root over (N.sub.1N.sub.2)}, and a phase of each element corresponds to a phase value of a phase shifter connected to each antenna element. ¶0180 - When the channel state indication information includes the quantized value of the estimation value ĥf of the channel, quantization of an amplitude value and an angle value or quantization of a real part and an imaginary part of each element in the estimation value ĥf of the channel may be included. For example, the quantized value of the estimation value ĥf of the channel may include a value obtained by quantizing an amplitude and a phase of at least one element in the estimation value ĥf of the channel, or include a value obtained by quantizing a real part and an imaginary part of at least one element in the estimation value ĥf of the channel).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Re. Claims 9 and 24, SNOPS and Lee teaches Claims 8 and 23.
Yet, SNOPS does not explicitly teach and the estimate of the channel is recovered by a channel estimation neural network based on the one or more quantized values.
However, in the analogous art, Lee explicitly teaches and the estimate of the channel is recovered by a channel estimation neural network based on the one or more quantized values (Fig. 2-3 & ¶0033 - As an illustrative example, a UE may measure one or more signal parameters (e.g., link quality) of reference signals received from a BS, and may send a channel state information (CSI) report to the BS. An example CSI report, may include, for example, one or more of: a RSRP (reference signal receive power); … a Precoder Matrix Indicator (PMI), which may indicate what a device (e.g., UE) estimates as a suitable precoder matrix based on the selected rank; and a Channel Quality Indication (or channel quality indicator) (CQI), which may express or indicate the BS-UE channel or link quality, as measured by the UE … For example, the gNB or BS may then adjust its UL transmission parameters (e.g., precoder, MCS, and/or a number of transmission layers, etc.) for transmitting to the UE, based on the received CSI report);
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teachings of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Yet, SNOPS and Lee do not explicitly teach the feedback report indicates one or more quantized values associated with a measurement of the RS;
However, in the analogous art, He explicitly teaches the feedback report indicates one or more quantized values associated with a measurement of the RS; (Fig. 1-5 & ¶0003 - In a new radio (NR) system, a beam training procedure between a base station and a terminal device is completed by using a channel state information (CSI) reporting procedure … The terminal device receives each reference signal configured by the base station, measures reference signal received power (RSRP) of the reference signal, and then reports reference signal indexes of several reference signals with relatively high RSRP and quantized RSRP values corresponding to the several reference signals. ¶0136 (Please see Fig. 3) - S305: The second device sends the channel state indication information to the first device. ¶0137 - S306: The first device receives the channel state indicator information reported by the second device).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Re. Claims 11, 19 and 26, SNOPS and Lee and He teach Claims 9, 17 and 24.
Yet, SNOPS and Lee do not explicitly teach a value of the measurement of the RS is a complex number; and the one or more quantized values include both a quantized real value of the measurement of the RS and a quantized imaginary value of the measurement of the RS.
However, in the analogous art, He explicitly teaches a value of the measurement of the RS is a complex number; and the one or more quantized values include both a quantized real value of the measurement of the RS and a quantized imaginary value of the measurement of the RS (Fig. 1-5 & ¶0097 - Each element in the analog precoding vector mk is a complex number, an amplitude of each element is 1/√{square root over (N1N2)}, and a phase of each element corresponds to a phase value of a phase shifter connected to each antenna element. ¶0180 - When the channel state indication information includes the quantized value of the estimation value ĥf of the channel, quantization of an amplitude value and an angle value or quantization of a real part and an imaginary part of each element in the estimation value ĥf of the channel may be included. For example, the quantized value of the estimation value ĥf of the channel may include a value obtained by quantizing an amplitude and a phase of at least one element in the estimation value ĥf of the channel, or include a value obtained by quantizing a real part and an imaginary part of at least one element in the estimation value ĥf of the channel).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Re. Claim 17, SNOPS and Lee teaches Claim 16.
Yet, SNOPS does not explicitly teach execution of the instructions further cause the apparatus to quantize one or more values associated with a measurement of the RS, wherein the feedback report indicates the one or more quantized values associated with the RS.
However, in the analogous art, Lee explicitly teaches execution of the instructions further cause the apparatus to quantize one or more values associated with a measurement of the RS, (Fig. 2-3 & ¶0033 - As an illustrative example, a UE may measure one or more signal parameters (e.g., link quality) of reference signals received from a BS, and may send a channel state information (CSI) report to the BS. An example CSI report, may include, for example, one or more of: a RSRP (reference signal receive power); … a Precoder Matrix Indicator (PMI), which may indicate what a device (e.g., UE) estimates as a suitable precoder matrix based on the selected rank; and a Channel Quality Indication (or channel quality indicator) (CQI), which may express or indicate the BS-UE channel or link quality, as measured by the UE … For example, the gNB or BS may then adjust its UL transmission parameters (e.g., precoder, MCS, and/or a number of transmission layers, etc.) for transmitting to the UE, based on the received CSI report);
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Lee to the teaching of SNOPS. The motivation would be because channel estimation with ML techniques (neural network models) may be useful for a variety of applications or circumstances, e.g., such as to offload the channel estimation to a neural network model (¶0040, Lee).
Yet, SNOPS and Lee do not explicitly teach wherein the feedback report indicates the one or more quantized values associated with the RS;
However, in the analogous art, He explicitly teaches wherein the feedback report indicates the one or more quantized values associated with the RS (Fig. 1-5 & ¶0003 - In a new radio (NR) system, a beam training procedure between a base station and a terminal device is completed by using a channel state information (CSI) reporting procedure … The terminal device receives each reference signal configured by the base station, measures reference signal received power (RSRP) of the reference signal, and then reports reference signal indexes of several reference signals with relatively high RSRP and quantized RSRP values corresponding to the several reference signals. ¶0136 (Please see Fig. 3) - S305: The second device sends the channel state indication information to the first device. ¶0137 - S306: The first device receives the channel state indicator information reported by the second device).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of He to the teachings of SNOPS and Lee. The motivation would be because the invention provides means for a base station to determine which spatial filtering parameters are used for sending signals in which directions, so that the terminal device can receive a relatively high-energy signal, and finally complete the beam training procedure (¶0003, He).
Claims 7, 14, 22 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over SNOPS and Lee and Li as applied to Claims 5-6, 12-13, 15, 20-21, 27-28 and 30 above, and further in view of Thomson (US7308232B2), Thomson hereinafter.
Re. Claims 7, 14, 22 and 29, SNOPS and Lee and Li teach Claims 6, 13, 21 and 28.
Yet, SNOPS and Lee do not explicitly teach each channel coefficient of the first group of channel coefficients corresponds to a classification score associated with a respective channel statistic of a plurality of channel statistics associated with the RS; each channel coefficient of the first group of channel coefficients is associated with a single respective antenna of a group of receiving antennas of the UE; and a value of each channel coefficient of the first group of channel coefficients is a product of the estimated channel associated with a respective antenna of the group of antennas and a transpose of a matrix associated with the codebook.
However, in the analogous art, Li explicitly teaches associated with a codebook (Fig. 1-5 & ¶0282 - After receiving the s first linear combination coefficient groups reported by the terminal device, the base station may further determine a W.sub.2 codebook corresponding to the s first linear combination coefficient groups. Please also see ¶0259, ¶0284 and ¶0289);
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Li to the teachings of SNOPS and Lee. The motivation would be because the invention provides a communication method, a base station, and a terminal device to improve channel feedback precision (¶0006, Li).
Yet, Li and SNOPS and Lee do not explicitly teach each channel coefficient of the first group of channel coefficients corresponds to a classification score associated with a respective channel statistic of a plurality of channel statistics associated with the RS; each channel coefficient of the first group of channel coefficients is associated with a single respective antenna of a group of receiving antennas of the UE; and a value of each channel coefficient of the first group of channel coefficients is a product of the estimated channel associated with a respective antenna of the group of antennas and a transpose of the matrix….
However, in the analogous art, Thomson explicitly teaches each channel coefficient of the first group of channel coefficients corresponds to a classification score associated with a respective channel statistic of a plurality of channel statistics associated with the RS; (Page 3, ¶8 - The data coefficients are estimated using a channel inversion. As part of this process, an estimate of the signal-to-noise ratios of individual data coefficients is simultaneously obtained, allowing more efficient use of codes. Page 9, ¶89 - As previously indicated, the present invention employs a channel estimation technique that employs a channel estimate based upon statistics of the channel, which is then updated based upon current channel estimates. Please also see Claim 1);
each channel coefficient of the first group of channel coefficients is associated with a single respective antenna of a group of receiving antennas of the UE; (Page 6, ¶41 - A number, s, of the coefficients, denoted by x(1), . . . x(s), are selected for channel estimation coefficients (CECs) and equation 22 is rewritten for these coefficients … Page 8, ¶82 - In the case of multiple antennas, the CEC's are replaced with CEC matrices. If one had, for example, J=3 antennas (representing, for example, separate polarizations) one could code the three data streams as follows…);
and a value of each channel coefficient of the first group of channel coefficients is a product of the estimated channel associated with a respective antenna of the group of antennas and a transpose of the matrix (Page 6, ¶44 - Because the x(x)'s are specified, one can do a least-squares solution of this equation to estimate c.sub.0 and c.sub.1, as follows: .. Please see equations 23A-23E, specifically equation 23B. Examiner interprets equation 23B as structurally and mathematically generating per-antenna coefficients using a product of a matrix and its transpose, where each coefficient represents a statistically computed measure of the channel).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to add the teaching of Thomson to the teachings of SNOPS and Lee and Li. The motivation would be because the invention relates to methods and apparatus for compensating for channel distortion and, more particularly, to a method and apparatus for equalizing a channel using improved channel estimates (Page 1, ¶2, Thomson).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He et al. (US 2023/0283337) – Please see Abstract, Fig. 1-12 and ¶0002-¶0503.
Medra et al. (US 2024/0080226) – Please see Abstract, Fig. 1-9 and ¶0002-¶0178.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSSA WILLIAMS whose telephone number is (571)270-7673. The examiner can normally be reached Mon-Fri 8-5pm. 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, Ayman Abaza can be reached on (571) 270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALYSSA WILLIAMS/Examiner, Art Unit 2465B
/AYMAN A ABAZA/Primary Examiner, Art Unit 2465
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