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 .
Claim Objections
Claims 1-14 and 19-24 are objected to because of the following informalities:
Claim 1, line 1: “method (400)” should read as “method”, “channel (200)” should read as “channel”.
The lengthy disclosure has not been checked to the extent necessary to determine the presence of all possible minor errors in claims. Applicant’s cooperation is requested in correcting any errors similar as claim 1 line 1 for claims 1-14 and 19-24.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 19-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 19-20 recite “An apparatus” as a machine to obtain and process channel data set, claims 21-22 recite “A control node comprising the apparatus of claim 19” and claim 23 recites “A wireless communication system comprising the control node of claim 21”. However, the claims do not recite any structure of the apparatus. It is not clear from the claim what particular structural component of the apparatus is performing the functions recited in the claim. Therefore, the claims are indefinite and are rejected under 35 U.S.C 112(b) or pre‐AIA 35 U.S.C 112, second paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 19-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yue et al. (US 20230051245 A1), hereinafter “Yue”.
Per claim 1, 19, 21, 23 and 24:
Regarding claim 19, Yue teaches ‘An apparatus (500)’ (Yue: [0002]: “apparatus”); ‘for modeling a wireless transmission channel (200)’ (Yue: [0002]: “apparatus for channel estimation and precoding with incomplete channel observations and channel state information (CSI) feedback”);
‘wherein the apparatus is configured to cause:
obtaining of a partial uplink, UL, channel data set (230)’ (Yue: [0005]: “In TDD communication systems, in order to obtain full DL channel information using UL measurements, a UE has to transmit reference signals, such as sounding reference signals (SRSs)”; [0094]: “obtain information about a DL is utilizing channel reciprocity on channel measurements based on UL channel sounding. The UL channel sounding based technique is usually used in TDD communication systems”; [0113]: “the UE can only transmit reference signals on a subset of its antennas. The channel matrix H is expressible as
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, where
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is the channel information determined from the reference signals transmitted by the UE, and
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is missing”, SRS can only provide partial UL channel
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corresponding to the part of channel space where SRS is transmitted, UL channel
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corresponding to the remaining part of channel space (the orthogonal subspace) is missing. By using channel reciprocity for TDD, the channel matrix H can be used for both UL reception and DL transmission);
‘obtaining of a partial downlink, DL, channel data set (220)’ (Yue: [FIG.4]: step 407: “RECEIVE CSI FEEDBACK”; [0002]: “incomplete channel observations and channel state information (CSI) feedback”; [0089]: “In CSI feedback, an access node transmits reference signals (such as CSI reference signals (CSI-RS)), which are used by a UE that receives the reference signals to make measurements of the DL channels. The UE generates channel information from the measurements and reports the channel information to the access node”, CSI feedback is corresponding to the part of channel space where CSI-RS is transmitted);
‘processing of the partial UL channel data set (230) and the partial DL channel data set (220) to provide reconstructed channel data set (210) for UL and DL’ (Yue: [FIG.8]: “CSI FEEDBACK”, “CHANNEL INFO FROM SRS MEASUREMENT
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”, block 809: “PROJECTION (PROJECT ONTO ORTHOGONAL SUBSPACE)” -> “Z”; [0167]: “a projection unit 809 configured to project channel information (e.g., the processed CSI feedback Ĥ) on the orthogonal subspace of the channel information derived from UL reference signals
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”; [0117]: “The channel matrix from the CSI feedback, projected onto the orthogonal subspace of
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is expressible as
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”; projection into orthogonal subspace to obtain missing matrix
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=> reconstructed channel matrix [
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“Z”] contains full channel matrix
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for both UL reception and DL transmission);
‘provisioning of said reconstructed channel data set (210) for UL and DL for subsequent communication in a wireless system (10)’ (Yue: [0102]: “Utilizing both the UL channel sounding based technique and the CSI feedback to perform channel estimation”; [0122]: “UL channels conveying the reference signals (e.g., SRS)”, reconstructed full channel matrix H= [
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“Z”] can be used for UL reception; [0115]: “W is the semi-unitary matrix arising from the SVD of H. W may be expressed as
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”; [0116]: “where
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are the dominant eigenvectors of
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”; [FIG.8]: “
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”; reconstructed full channel matrix H= [
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“Z”] can be used to determine precoding matrix
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for DL transmission; [0108]: “W is the precoding matrix used to precode transmissions”).
Regarding claim 1, claim 1 recites the method implemented by the apparatus of claim 19 (see rejection of claim 19 above).
Regarding claim 21, Yue teaches ‘A control node (20)’ (Yue: [0059]: “control nodes, base stations”; [FIG.10]: “BASE STATION”); ‘comprising the apparatus (500) of claim 19’ (see rejection of claim 19 above).
Regarding claim 23, Yue teaches ‘A wireless communication system (10)’ (Yue: [FIG.10]: communication system); ‘comprising the control node (20) of claim 21’ (see rejection of claim 21 above).
Regarding claim 24, Yue teaches ‘A computer program product (600) comprising a non-transitory computer readable medium (610)’ (Yue: [FIG.11B]: “MEMORY”; [Claim 16]: “a non-transitory memory storage”); ‘having thereon a computer program (620) comprising program instructions (625)’ (Yue: [Claim 16]: “a non-transitory memory storage comprising instructions”); ‘the computer program (620) being loadable into a data processing (700) unit and configured to cause execution of’ (Yue: [FIG.11B]: “”PROCESSING UNIT”; [Claim 16]: “one or more processors in communication with the non-transitory memory storage, wherein the one or more processors execute the instructions”); ‘the method (400) according to claim 1’ (see rejection of claim 1 above); ‘when the computer program (520) is run by the data processing unit (700)’ (Yue: [Claim 16]: “the one or more processors execute the instructions to cause the node to”).
Regarding claim 2, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein the partial UL channel data set (230) is a partial UL channel matrix (230) obtained based one or more Sounding Reference Signals, SRS, relating to the UL’ (Yue: [0005]: “In TDD communication systems, in order to obtain full DL channel information using UL measurements, a UE has to transmit reference signals, such as sounding reference signals (SRSs)”; [0113]: “the UE can only transmit reference signals on a subset of its antennas. The channel matrix H is expressible as
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, where
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is the channel information determined from the reference signals transmitted by the UE, and
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is missing”).
Regarding claim 3, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein the partial DL channel data set (220) is obtained based one or more channel state information, CSI, feedback relating to the DL’ (Yue: [FIG.4]: step 407: “RECEIVE CSI FEEDBACK”, [0002]: “incomplete channel observations and channel state information (CSI) feedback”; [0089]: “In CSI feedback, an access node transmits reference signals (such as CSI reference signals (CSI-RS)), which are used by a UE that receives the reference signals to make measurements of the DL channels. The UE generates channel information from the measurements and reports the channel information to the access node”).
Regarding claim 20, this claim is the same as claim 19 since Examiner interprets, with BRI, “to perform the method (400) of any one of the claims 1 to 18” as “to perform the method (400) of claim 1” (see rejection of claim 19 above).
Regarding claim 22, Yue teaches the control node (20) of claim 21 (discussed above).
Yue teaches ‘wherein the control node (20) is a network node (20)’ (Yue: [0059]: “base stations”).
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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Yue, in view of Manolakos et al. (US 20240061069 A1), hereinafter “Manolakos”.
Regarding claim 4, Yue teaches the method (400) of claim 3 (discussed above).
Yue does not expressly teach, but Manolakos in the same field of endeavor teaches ‘wherein the CSI feedback is Type-I codebook based feedback’ (Manolakos: [0162]: “the NR Type I single-panel codebook is a constant modulus DFT codebook tailored for a dual-polarized 2D UPA”; [0165]: “This is similar to the CSI feedback procedure. For example, in 3GPP procedures on CSI feedback”); ‘Type-II codebook based feedback’ (this is optional).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Manolakos’s teaching with that of Yue in order to promote collaboration and inter-operation by conforming to 3GPP procedure on CSI feedback (see reference quotes in element above).
Regarding claim 11, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein said subsequent communication (460) in the wireless system (10) comprises beamforming (465) at least one of a UL transmission’ (this is optional);
‘a DL transmission based on the reconstructed channel data set (210)’ (Yue: [0102]: “Utilizing both the UL channel sounding based technique and the CSI feedback to perform channel estimation”; [0122]: “UL channels conveying the reference signals (e.g., SRS)”, reconstructed full channel matrix H= [
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“Z”] can be used for UL reception; [0115]: “W is the semi-unitary matrix arising from the SVD of H. W may be expressed as
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”; [0116]: “where
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are the dominant eigenvectors of
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”; [FIG.8]: “
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”; reconstructed full channel matrix H= [
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“Z”] can be used to determine precoding matrix
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for DL transmission; [0108]: “W is the precoding matrix used to precode transmissions”). However, Yue fails to expressly teach beamforming a DL transmission.
Manolakos teaches beamforming a DL transmission (Manolakos: [FIG.4]; [0120]: “the base station 402 may transmit a beamformed signal to the UE”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Manolakos’s teaching with that of Yue in order to extend signal range and reduce interference by beamforming to focus radio waves directly at specific devices.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Yue, in view of Yu Zhao (“Channel Reconstruction for High-Rank User Equipment”), hereinafter “Yu”.
Regarding claim 5, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein processing (440) the partial UL channel data set (230) and the partial DL channel data set (220) applying (445) a generative model (300) comprising a generative function (310) with the partial UL channel data set (230) and the partial DL channel data set (220) as inputs’ (Yue: [FIG.8]: “CSI FEEDBACK”, “CHANNEL INFO FROM SRS MEASUREMENT
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”, block 809: “PROJECTION (PROJECT ONTO ORTHOGONAL SUBSPACE)” -> “Z”; [0167]: “a projection unit 809 configured to project channel information (e.g., the processed CSI feedback Ĥ) on the orthogonal subspace of the channel information derived from UL reference signals
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”; [0117]: “The channel matrix from the CSI feedback, projected onto the orthogonal subspace of
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is expressible as
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”; projection into orthogonal subspace to obtain missing matrix
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=> reconstructed full channel matrix H=[
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“Z”] from input of partial UL channel info from SRS and partial DL channel info from CSI feedback ). However, Yue fails to expressly teach a generative model.
Yu in the same field of endeavor teaches applying a feed-forward neural network (generative function) to reconstruct channel matrix with SRS and CSI feedback as inputs (Yu: [Page 39]: “a feed-forward network”; [Page 51, Figure 4-2]: “
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”; applying a feed-forward neural network (a generative function) with SRS
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and CSI feedback as input to reconstruct ideal precoding matrix W and channel matrix
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; [Page 20]: “a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s teaching with that of Yue in order to apply a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information (see reference quotes in element above).
Regarding claim 6, combination of Yue and Yu teaches the method (400) of claim 5 (discussed above).
Yue does not expressly teach, but Yu teaches ‘wherein the generative function (310) is a feedforward neural network (310)’ (Yu: [Page 39]: “a feed-forward network”; [Page 51, Figure 4-2]; [Page 20]: “a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s teaching with that of Yue in order to apply a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information (see reference quotes in element above).
Regarding claim 7, combination of Yue and Yu teaches the method (400) of claim 5 (discussed above).
Yue does not expressly teach, but Yu teaches ‘training (410) the generative model (300)’ (Yu: [Page 75]: “model_x.fit (xTrain, yTrain … model_x.save_weights”, training the model until convergence and save neural network weights for generative function (feed-forward network); [Page 35]: “the channel state information generated … trained with 300000 different observations”; [Page 20]: “a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s teaching with that of Yue in order to apply a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information (see reference quotes in element above).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Yue and Yu, in view of Balevi et al. (“High Dimensional Channel Estimation Using Deep Generative Networks”) (IDS cited), hereinafter “Balevi.
Regarding claim 8, combination of Yue and Yu teaches the method (400) of claim 7 (discussed above).
Combination of Yue and Yu teaches ‘wherein training (410) the generative model (300) comprises iteratively’ (Yu: [Page 75]: “model_x.fit (xTrain, yTrain”, training the model; [Page 37]: “the stopping criterion is that the loss function reaches its global Minimum … A large learning rate makes the algorithm converge within several iterations”, iteratively until convergence);
‘mapping (413) a channel data set target (X) to a hybrid channel data set (Y) comprising UL channel data (235) not comprised in the partial UL channel data set (230)’ (Yue: [0113]: “the UE can only transmit reference signals on a subset of its antennas. The channel matrix H is expressible as
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, where
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is the channel information determined from the reference signals transmitted by the UE, and
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is missing”; [0117]: “The channel matrix from the CSI feedback, projected onto the orthogonal subspace of
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is expressible as
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”; projection into orthogonal subspace to obtain missing matrix
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=> reconstructed channel matrix [
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“Z”] contains full channel matrix
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);
‘DL channel data (225) not comprised in the partial DL channel data set (220)’ (this is optional);
‘mapping (417) the channel data set target (X), the partial UL channel data set (230) and the partial DL channel data set (220) to a probability measure (P)’ (Yue: [FIG.8]: “CSI FEEDBACK”, “CHANNEL INFO FROM SRS MEASUREMENT ”, block 809: “PROJECTION (PROJECT ONTO ORTHOGONAL SUBSPACE)” -> “Z”. Yu: [Page 51, Figure 4-2]: “
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”; [Page 73]: “udAccuracy(yTrue, yPred)”, measure the accuracy of predicted (target) value with respect to true value for the neural network training; [Page 38]: “stochastic gradient descent”, a probability based neural network training). However, combination of Yue and Ye fails to expressly teach a probability measure;
‘until a convergence criterion is met and then: providing (418) a generative function (310) based on the hybrid data set (Y)’ (Yu: [Page 39]: “a feed-forward network”; [Page 75]: “model_x.fit (xTrain, yTrain … model_x.save_weights”, training the model until convergence and save neural network weights for generative function (feed-forward network)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s teaching of feed-forward network with that of Yue in order to apply a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information (Yu: [Page 20]: “a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information”).
Balevi in the same field of endeavor teaches probability measure for Stochastic Gradient Descent Training (Balevi: [Page 21, Col 1]: “D(x) represents the probability that x came from the data rather than G”; [Page 21, Col 2]: “Algorithm 1: Minibatch Stochastic Gradient Descent Training of Wasserstein GANs for Spatial Channel Matrix Generation … by descending its stochastic gradient
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”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balevi’s teaching with that of combination of Yue and Yu to map the channel data set target (X), the partial UL channel data set (230) and the partial DL channel data set (220) to a probability measure (P) in order to maximize the correlation between the received signal and the generator’s channel estimate while minimizing the rank of the channel estimate (Balevi: [Abstract]: “propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator’s channel estimate while minimizing the rank of the channel estimate”).
Regarding claim 9, combination of Yue, Yu and Balevi teaches the method (400) of claim 8 (discussed above).
Combination of Yue and Yu teaches ‘wherein mapping (413) the channel data set target (X) further comprises mapping (415) the channel data set target (X) to a random variable (Z)’ (Yue: [FIG.8]: “Channel COVARIANCE”; “CSI FEEDBACK”, “CHANNEL INFO FROM SRS MEASUREMENT
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”, block 809: “PROJECTION (PROJECT ONTO ORTHOGONAL SUBSPACE)” -> “Z”, map channel data set target to channel matrix [
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“Z”]. Yu: [Page 33]: “random variables”; [Page 34]: “achieve randomness of the parameters”; [Page 37]: “initializes all the weights with random numbers based on different parameter initialization algorithm”; map channel data set target to a random variable; [Page 51, Figure 4-2]: “
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”; Page 39]: “a feed-forward network”; [Page 20]: “a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s teaching of feed-forward network with that of Yue in order to apply a reliable machine learning model using a partial channel state information to help reconstruct the full channel state information (see reference quotes in element above).
Regarding claim 10, combination of Yue, Yu and Balevi teaches the method (400) of claim 9 (discussed above).
Combination of Yue and Yu does not expressly teach, but Balevi teaches ‘wherein the random variable (Z) is sampled from a multi-dimensional Gaussian distribution’ (Balevi: [Page 20, Col 1]: “where each element of
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are independent and identically distributed complex Gaussian random variables with mean 0 and variance
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”, random variable from multi-dimensional (
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dimensions) Gaussian distribution; [Abstract]: “propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator’s channel estimate while minimizing the rank of the channel estimate”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balevi’s teaching with that of combination of Yue and Yu in order to maximize the correlation between the received signal and the generator’s channel estimate while minimizing the rank of the channel estimate (see reference quotes in element above).
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yue, in view of Walton et al. (US 20020154705 A1), hereinafter “Walton”.
Regarding claim 12, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein the transmission channel (200) comprises a plurality of sub-channels (205) and the partial UL channel data set (230) is limited to a subset of said plurality of sub-channels (205)’ (Yue: [0113]: “the UE can only transmit reference signals on a subset of its antennas. The channel matrix H is expressible as
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, where
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is the channel information determined from the reference signals transmitted by the UE, and
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is missing”). However, Yue fails to expressly teach a plurality of sub-channels.
Walton in the same field of endeavor teaches a plurality of sub-channels (Walton: [FIG.2]: “sub-channel 1” – “sub-channel 16”; [0016]: “The multi-carrier modulation partitions the system operating bandwidth, W, into a number of (L) sub-bands. Each sub-band is associated with a different center frequency and corresponds to one sub-channel”; [Title]: “High Efficiency High Performance Communications System Employing Multi-carrier Modulation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Walton’s teaching with that of Yue for the transmission channel (200) to comprise a plurality of sub-channels (205) and the partial UL channel data set (230) is limited to a subset of said plurality of sub-channels (205) in order to achieve high efficiency high performance communications system (see reference quotes in element above).
Regarding claim 13, Yue teaches the method (400) of claim 1 (discussed above).
Yue teaches ‘wherein the transmission channel (200) comprises a plurality of sub-channels (205) and the partial DL channel data set (220) is limited to a subset of said plurality of sub-channels (205)’ (Yue: [FIG.4]: step 407: “RECEIVE CSI FEEDBACK”; [0002]: “incomplete channel observations and channel state information (CSI) feedback”; [0089]: “In CSI feedback, an access node transmits reference signals (such as CSI reference signals (CSI-RS)), which are used by a UE that receives the reference signals to make measurements of the DL channels. The UE generates channel information from the measurements and reports the channel information to the access node”, CSI feedback is corresponding to the part of channel space where CSI-RS is transmitted). However, Yue fails to expressly teach a plurality of sub-channels.
Walton in the same field of endeavor teaches a plurality of sub-channels (Walton: [FIG.2]: “sub-channel 1” – “sub-channel 16”; [0016]: “The multi-carrier modulation partitions the system operating bandwidth, W, into a number of (L) sub-bands. Each sub-band is associated with a different center frequency and corresponds to one sub-channel”; [Title]: “High Efficiency High Performance Communications System Employing Multi-carrier Modulation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Walton’s teaching with that of Yue for the transmission channel (200) to comprise a plurality of sub-channels (205) and the partial DL channel data set (220) is limited to a subset of said plurality of sub-channels (205) in order to achieve high efficiency high performance communications system (see reference quotes in element above).
Regarding claim 14, combination of Yue and Walton teaches the method (400) of claim 12 (discussed above).
Yue does not expressly teach, but Walton teaches ‘wherein the sub-channels (205) are configured with different center frequencies (fc)’ (Walton: [FIG.2]: “sub-channel 1” – “sub-channel 16”; [0016]: “The multi-carrier modulation partitions the system operating bandwidth, W, into a number of (L) sub-bands. Each sub-band is associated with a different center frequency and corresponds to one sub-channel”; [Title]: “High Efficiency High Performance Communications System Employing Multi-carrier Modulation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Walton’s teaching with that of Yue in order to achieve high efficiency high performance communications system (see reference quotes in element above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190354856 A1 see [0083];
US 20210103794 A1) see [0033];
“Towards FDD Massive MIMO: Downlink Channel Covariance Matrix Estimation Using Conditional Generative Adversarial Networks” (IDS cited) see [Page 940]-[Page 945];
“Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN” (IDS cited) see [Page 854]-[Page 858].
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/G.F./Examiner, Art Unit 2462
/YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462
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