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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2024 and 05/03/2024 have been considered by the examiner and been placed of record in the file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to Independent claims 1, 8, 14 and 20:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 1, Yes, the claim is a process.
Step 2A Prong One Analysis: Do the claims recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitations “estimating channel coefficients of interference-and-noise (Rz) matrix … determining a covariance of interference-and-noise… determining a noise variance (σ2) based on noise measurements” recited in independent claims 1, 8, 14 and 20 is the abstract idea of a mathematical relationship. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitations “determining an optimal equalizer from a plurality of equalizers, based on diagonal elements of the (Rz) matrix and (σ2) of the at least one RB using an artificial intelligence (AI) model” recited in independent claims 1 and 14 are an additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses an algorithm in its ordinary capacity as a tool to perform an existing process and select an “optimal equalizer”. Also limitations: “determining an optimal equalizer from a plurality of equalizers based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB”, cited in claims 8 and 20, are just application of more mathematical expression to make ma decision. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitations “determining an optimal equalizer from a plurality of equalizers, based on diagonal elements of the (Rz) matrix and (σ2) of the at least one RB using an artificial intelligence (AI) model” recited in independent claim 1 are an additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses an algorithm in its ordinary capacity as a tool to perform an existing process and select an “optimal equalizer”. See MPEP § 2106.05(f)(2).
As to claims 2, 9 and 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 2 and 9, Yes, the claims are a process.
For claim 15, Yes, the claim is a machine.
Step 2A Prong One Analysis: Do the claims recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the plurality of equalizers comprises at least one of a minimum mean squared error (MMSE) equalizer, MMSE with interference rejection combiner (MMSE-IRC) equalizer, or an MMSE with successive interference cancellation (MMSE-SIC) equalizer”, is a continuation of the abstract idea in the parent claims.
Step 2A Prong Two Analysis: Do the claims recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Do the claims recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 3 and 16:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 3, Yes, the claims are a process.
For claim 16, Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the interference comprises at least one of a co-channel interference, or inter-layer interference (ILI)”, is merely the data being used to select an optimal equalizer which is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claims 4, 13 and 17:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 4 and 13, Yes, the claims are a process.
For claim 17, Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitations included in claims 4, 13 and 17, are merely steps of training an AI algorithm or estimating covariance of interference and noise is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 5, 12 and 18:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claims 5 and 12, Yes, the claims are a process.
For claim 18, Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitations merely cite the data (noise or BLER being used to select an optimal equalizer which is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claims 6 and 19:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 6, Yes, the claims are a process.
For claim 19, Yes, the claim is a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the AI model comprises M+1 input layers and one or more output layers, and wherein “M” indicates antennas at the BS”, is merely the number of layers in a neural network which is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claims 10 and 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claims 10 and 11, Yes, the claims are a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation, is merely cite the type equalizer to be selected which is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
As to claim 7
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
For claim 7, Yes, the claim is a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the BS is one of a distributed BS or a centralized BS”, is merely the type of base station being used which is a continuation of the an abstract idea in the parent claims.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, all elements are part of the abstract idea as shown above.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, all elements are part of the abstract idea as shown above.
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.
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.
Claims 1-2, 4-11, 13-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huh et al. (US 2018/0110015 A1) in view of Jeon et al. (US 20230082053 A1).
Claim 1. Huh et al. disclose A method performed by a base station (BS) in a wireless communication system (read as a method of selectively using an interference rejection combining (IRC) receiver [0009]), the method comprising:
estimating channel coefficients of each slot of a plurality of slots (read as In a single-input multiple-output (SIMO) case, before an IRC receiver is applied, an MRC receiver may be defined by Equation 3… ŝMRC may be an MRC estimate of a transmitted symbol, Ĥ may be an estimate of an N×M channel matrix, and r may be a received symbol vector [0072-0073]. FIG. 3) based on received demodulation reference signal (DM-RS) symbols;
determining a covariance of interference-and-noise (Rz) matrix for at least one resource block (RB) of a plurality of RBs (FIG. 3) of each slot based on the channel coefficients (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]);
determining a noise variance (σ2) based on noise measurements performed for one or more sub-carriers without the interference (read as … σ2 may be the sum of average interference and noise power [0078]); and
determining an optimal (the term “optimal” is not explicitly defined) equalizer from a plurality of equalizers (read as determine whether to use the IRC receiver [0093]. Determining the equalizer coefficients is equivalent to selecting one equalizer.), based on diagonal elements of the Rz matrix and σ2 of the at least one RB (read as When the estimated DIP is higher than the DIP threshold, the UE may determine to use the IRC receiver [0093]) using an artificial intelligence (AI) model.
Huh et al. do not explicitly disclose receiving reference signal and using artificial intelligence to performed equalization. However, in the related field of endeavor Jeon et al. disclose: a base station for receiving data in a wireless communication system, including a receiver; a transmitter; and a processor. The receiver receives a channel signal and a reference signal (RS) from the base station, and the processor performs an operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0020] … RS may be a demodulation reference signal (DMRS) [0029].
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of Huh et al. with the teaching of Jeon et al. in order to provide an implementable MIMO detector with optimal performance may be designed even when the number of antennas and the number of users increase (Jeon et al. [0033]).
Claim 2. The method of claim 1, the combination of Huh et al. and Jeon et al. teaches,
wherein the plurality of equalizers comprises at least one of a minimum mean squared error (MMSE) equalizer (Huh et al.: read as minimum mean square error (MMSE) receiver [0076]), MMSE with interference rejection combiner (MMSE-IRC) equalizer, or an MMSE with successive interference cancellation (MMSE-SIC) equalizer.
Claim 4. The method of claim 1, the combination of Huh et al. and Jeon et al. teaches,
wherein the AI model is trained by:
generating input features comprising a plurality of training diagonal elements of Rz and over σ2 of each RB (Huh et al.: read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]), wherein the training diagonal elements are obtained based on training (Jeon et al.: read as the training data is input to the ANN [0011]) channel coefficients of each slot based on training dataset of DM-RS symbols (Jeon et al.: read as The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0020] … RS may be a demodulation reference signal (DMRS) [0029]);
performing equalization on each slot using each of the plurality of equalizers (Jeon et al.: read as The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0020] … RS may be a demodulation reference signal (DMRS) [0029]), on the training dataset (Jeon et al.: read as the training data is input to the ANN [0011]);
obtaining decoded bits for each of the equalizers by performing a predefined decoding technique (Jeon et al.: read as operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence [0020]);
determining numbers of error bits for each slot generated by each of the plurality of equalizers during equalization based on the respective decoded bits (read as channel coding method according to the present disclosure may include attaching a cyclic redundancy check (CRC) code to a transport block … perform rate matching of the encoded code blocks [0156]); and
determining output labels for the AI model for each slot based on a comparison of the number of error bits corresponding to each of the plurality of equalizers with respect to each other (read as operation of equalizing the RS based on a channel RS to generate a sequence; and decoding the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0021]. Generating different equalizer coefficient is equivalent to selecting an equalizer.).
Claim 5. The method of claim 1, the combination of Huh et al. and Jeon et al. teaches,
wherein the AI model is trained (Jeon et al.: steps of training the neural network is disclosed [0165-0177, also FIG. 6-13) based on a correlation between interference proportion and operating signal to interference noise ratio (SINR) associated with a plurality of training diagonal elements (Huh et al.: … defined as the ratio between the power of diagonal elements in the covariance matrix and the power of off-diagonal elements in the covariance matrix [0084]).
Claim 6. The method of claim 1, the combination of Huh et al. and Jeon et al. teaches,
wherein the AI model comprises M+1 input layers and one or more output layers (Jeon et al.: FIG. 7-9), and wherein “M” indicates antennas at the BS (Jeon et al. FIG. 8, shown AI model with several layers which could be mapped to the numbers of antennas.).
Claim 7. The method of claim 1, the combination of Huh et al. and Jeon et al. teaches,
wherein the BS is one of a distributed BS or a centralized BS (Jeon et al. base station [0020]).
Claim 8. Huh et al. disclose A method performed by a base station (BS) in a wireless communication system (FIG. 1), the method comprising:
estimating channel coefficients of each slot of a plurality of slots (read as In a single-input multiple-output (SIMO) case, before an IRC receiver is applied, an MRC receiver may be defined by Equation 3… ŝMRC may be an MRC estimate of a transmitted symbol, Ĥ may be an estimate of an N×M channel matrix, and r may be a received symbol vector [0072-0073]. FIG. 3) with respect to time based on received demodulation reference signal (DM-RS) symbols;
determining a covariance of interference-and-noise (Rz) matrix for at least one resource block (RB) of a plurality of RBs of each slot based on the channel coefficients (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]);
determining a noise variance (σ2) based on noise measurements performed on one or more sub-carriers without the interference (read as … σ2 may be the sum of average interference and noise power [0078]);
estimating an interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix and the noise variance (σ2) (read as … σ2 may be the sum of average interference and noise power [0078]); and
determining an optimal (the term “optimal” is not explicitly defined) equalizer from a plurality of equalizers (read as determine whether to use the IRC receiver [0093]) based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB (read as When the estimated DIP is higher than the DIP threshold, the UE may determine to use the IRC receiver [0093]).
Huh et al. do not explicitly disclose receiving reference signal to performed equalization. However, in the related field of endeavor Jeon et al. disclose: a base station for receiving data in a wireless communication system, including a receiver; a transmitter; and a processor. The receiver receives a channel signal and a reference signal (RS) from the base station, and the processor performs an operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS [0020]… RS may be a demodulation reference signal (DMRS) [0029].
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of Huh et al. with the teaching of Jeon et al. in order to provide an implementable MIMO detector with optimal performance may be designed even when the number of antennas and the number of users increase (Jeon et al. [0033]).
Claim 9. The method of claim 8, the combination of Huh et al. and Jeon et al. teaches,
wherein the plurality of equalizers comprises at least one of a minimum mean squared error (MMSE) equalizer (Huh et al. read as minimum mean square error (MMSE) receiver [0076]), MMSE with interference rejection combiner (MMSE-IRC) equalizer, or an MMSE with successive interference cancellation (MMSE-SIC) equalizer.
Claim 10. The method of claim 9, the combination of Huh et al. and Jeon et al. teaches,
wherein, in case that the estimated interference proportion is less than the predetermined interference threshold (Huh et al.: read as the DIP threshold or less, the UE may determine not to use the IRC receiver. When the estimated DIP is higher than the DIP threshold, the UE may determine to use the IRC receiver [0012]), the optimal equalizer is determined to be the MMSE (Huh et al.: read as minimum mean square error (MMSE) receiver [0076]).
Claim 11. The method of claim 9, the combination of Huh et al. and Jeon et al. teaches,
wherein, in case that the estimated interference proportion is more than the predetermined interference threshold, the optimal equalizer is determined to be the MMSE-IRC (Huh et al.: read as a method of selectively using an interference rejection combining (IRC) receiver [0009]).
Claim 13. The method of claim 8, the combination of Huh et al. and Jeon et al. teaches,
wherein estimating the interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix and the noise variance (σ2) (Huh et al.: read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) comprises:
identifying diagonal elements from the covariance of interference-and-noise (Rz) matrix (Huh et al.: read as x may be the power of diagonal elements in the covariance matrix [0085]), wherein the diagonal elements is indicative of interference-plus-noise power (Huh et al.: read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) across each receiver antennas (Huh et al.: read as a plurality of transmission antennas and a plurality of reception antennas [0035]);
estimating interference-plus-noise power based on an average of the diagonal elements (Huh et al.: read as x may be the power of diagonal elements in the covariance matrix [0085]);
estimating interference power based on a function of the interference-plus-noise power and the noise variance (σ2) (Huh et al.: read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]); and
estimating the interference proportion based on a ratio of the estimated interference power and the estimated interference-plus-noise power (Huh et al.: equation 7).
Claim 14. Huh et al. disclose A base station (BS) in a wireless communication system, the BS comprising:
memory storing (Huh et al.: read as a memory 1202 [0108]) one or more computer programs (Huh et al.: read as software [0109]); and
one or more processors communicatively coupled to the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors (read as implemented in software, the aforementioned methods can be implemented with a module (i.e., process, function, etc.) for performing the aforementioned functions. The module may be stored in the memory and may be performed by the processor. [0109]), cause the BS to:
estimate channel coefficients of each slot of a plurality of slots (read as In a single-input multiple-output (SIMO) case, before an IRC receiver is applied, an MRC receiver may be defined by Equation 3… ŝMRC may be an MRC estimate of a transmitted symbol, Ĥ may be an estimate of an N×M channel matrix, and r may be a received symbol vector [0072-0073]. FIG. 3) with respect to time based on received demodulation reference signal (DM-RS) symbols,
determine a covariance of interference-and-noise (Rz) matrix (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) for at least one resource block (RB) (FIG. 3, resource blocks shown. Measurements has to be done on at least one RB) of a plurality of RBs of each slot based on the channel coefficients,
determine a noise variance (σ2) based on noise measurements performed (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) on one or more free sub-carriers without the interference (read as x may be the power of diagonal elements in the covariance matrix [0085]. Diagonal element usually represent the signal without noise), and
determine an optimal equalizer from a plurality of equalizers for managing the interference based on diagonal elements (read as x may be the power of diagonal elements in the covariance matrix [0085]) of the Rz matrix and σ2 (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) of the at least one RB using an artificial intelligence (AI) model.
Huh et al. do not explicitly disclose receiving reference signal and using artificial intelligence to performed equalization. However, in the related field of endeavor Jeon et al. disclose: a base station for receiving data in a wireless communication system, including a receiver; a transmitter; and a processor. The receiver receives a channel signal and a reference signal (RS) from the base station, and the processor performs an operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0020] … RS may be a demodulation reference signal (DMRS) [0029].
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of Huh et al. with the teaching of Jeon et al. in order to provide an implementable MIMO detector with optimal performance may be designed even when the number of antennas and the number of users increase (Jeon et al. [0033]).
Claim 15. The BS of claim 14, the combination of Huh et al. and Jeon et al. teaches,
wherein the plurality of equalizers comprises at least one of a minimum mean squared error (MMSE) equalizer (Huh et al.: read as minimum mean square error (MMSE) receiver [0076]), MMSE with interference rejection combiner (MMSE-IRC) equalizer, or an MMSE with successive interference cancellation (MMSE-SIC) equalizer.
Claim 17. The BS of claim 14, the combination of Huh et al. and Jeon et al. teaches,
wherein, to train the AI model (Jeon et al.: read as training the ANN [0011]), the one or more computer programs further include computer-executable instructions that, when executed by the one or more processors (Jeon et al.: read as one computer program including instructions that, when executed by at least one processor [0022]), cause the BS to:
generate input features comprising a plurality of training diagonal elements of Rz and σ2 of each RB (Huh et al.: read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]), wherein the training diagonal elements (read as x may be the power of diagonal elements in the covariance matrix [0085]) are obtained based on training channel coefficients of each slot based on training dataset of DM-RS symbols (Jeon et al.: read as The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0020] … RS may be a demodulation reference signal (DMRS) [0029]),
perform equalization on each slot using each of the plurality of equalizers, on the training dataset (read as operation of equalizing the RS based on a channel RS to generate a sequence; and decoding the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0021]. Generating different equalizer coefficient is equivalent to selecting an equalizer.),
obtain decoded bits for each of the equalizers by performing a predefined decoding technique (Jeon et al.: read as operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence [0020]),
determine numbers of error bits for each slot generated by each of the plurality of equalizers during equalization based on the respective decoded bits (read as channel coding method according to the present disclosure may include attaching a cyclic redundancy check (CRC) code to a transport block … perform rate matching of the encoded code blocks [0156]), and
determine output labels for the AI model for each slot based on a comparison of the number of error bits of each of the plurality of equalizers (read as operation of equalizing the RS based on a channel RS to generate a sequence; and decoding the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS is based on a parameter determined according to a machine learning process [0021]. Generating different equalizer coefficient is equivalent to selecting an equalizer.).
Claim 18. The BS of claim 14, the combination of Huh et al. and Jeon et al. teaches,
wherein the one or more computer programs further include computer-executable instructions that, when executed by the one or more processors, cause the BS to train the AI model based on a correlation between interference proportion and operating signal to interference noise ratio (SINR) associated with a plurality of training diagonal elements (Huh et al.: read as minimum mean square error (MMSE) receiver [0076]).
Claim 19. The BS of claim 14, the combination of Huh et al. and Jeon et al. teaches,
wherein the AI model comprises M+1 input layers and one or more output layers (Jeon et al.: FIG. 7-9), and wherein “M” indicates antennas at the BS (Jeon et al. FIG. 8, shown AI model with several layers which could be mapped to the numbers of antennas.).
Claim 20. Huh et al. disclose A base station (BS) (FIG. 1) in a wireless communication system , the BS comprising:
memory storing one or more computer programs (read as implemented in software, the aforementioned methods can be implemented with a module (i.e., process, function, etc.) for performing the aforementioned functions. The module may be stored in the memory and may be performed by the processor. [0109]); and
one or more processors communicatively coupled to the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors (read as implemented in software, the aforementioned methods can be implemented with a module (i.e., process, function, etc.) for performing the aforementioned functions. The module may be stored in the memory and may be performed by the processor. [0109]), cause the BS to:
estimate channel coefficients of each slot of a plurality of slots (read as In a single-input multiple-output (SIMO) case, before an IRC receiver is applied, an MRC receiver may be defined by Equation 3… ŝMRC may be an MRC estimate of a transmitted symbol, Ĥ may be an estimate of an N×M channel matrix, and r may be a received symbol vector [0072-0073]. FIG. 3) with respect to time based on received demodulation reference signal (DM-RS) symbols;
determine a covariance (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]) of interference-and-noise (Rz) matrix for at least one resource block (RB) (read as may calculate a DIP estimation parameter based on a covariance matrix [0084]) of a plurality of RBs (FIG. 3, plurality of resource blocks) of each slot based on the channel coefficients;
determine a noise variance (σ2) based on noise measurements performed on one or more sub-carriers (read as time domain and a plurality of subcarriers [0038]) without the interference (read as A covariance matrix of interference and noise may be represented by Equation 2. [0069]);
estimate an interference proportion for the at least one RB based on the covariance of interference-and-noise (Rz) matrix (read as may calculate a DIP estimation parameter based on a covariance matrix [0084]) and the noise variance (σ2); and
determine an optimal equalizer from a plurality of equalizers based on a comparison of the interference proportion with a predetermined interference threshold for the at least one RB.
Huh et al. do not explicitly disclose receiving reference signal to performed equalization. However, in the related field of endeavor Jeon et al. disclose: a base station for receiving data in a wireless communication system, including a receiver; a transmitter; and a processor. The receiver receives a channel signal and a reference signal (RS) from the base station, and the processor performs an operation of equalizing the RS based on a channel RS to generate a sequence and decodes the received channel signal based on the generated sequence. The equalizing the RS based on the channel RS [0020] …. RS may be a demodulation reference signal (DMRS) [0029].
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of Huh et al. with the teaching of Jeon et al. in order to provide an implementable MIMO detector with optimal performance may be designed even when the number of antennas and the number of users increase (Jeon et al. [0033]).
Claims 3, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Huh et al. (US 2018/0110015 A1) and Jeon et al. (US 20230082053 A1) in view of Balraj et al. (US 2014/0010275 A1).
Claim 3. The method of claim 1, the combination of Huh et al. and Jeon et al. does not explicitly disclose,
wherein the interference comprises at least one of a co-channel interference, or inter-layer interference (ILI).
However, in the related field of endeavor Balraj et al. disclose: … the channel equalized signal is processed on the basis of a first precoding matrix such that the co-channel interference is mitigated [0052]. FIG. 5 all steps.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of the combination of Huh et al. and Jeon et al. with the teaching of Balraj et al. in order to improve data detection in a receiver circuit (Balraj et al. [0002]).
Claim 12. The method of claim 8, the combination of Huh et al. and Jeon et al. does not explicitly disclose,
wherein the predetermined interference threshold is determined based on block error rate (BLER) performance measurements and predefined configurations of BS.
However, in the related field of endeavor Balraj et al. disclose: FIGS. 6A to 9B are graphs that schematically illustrate performances of various receiver circuits including different types of equalizers. In each of FIGS. 6A to 9B, a Block Error Rate (BLER) is plotted against an average SNR in dB [0053].
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of the combination of Huh et al. and Jeon et al. with the teaching of Balraj et al. in order to improve data detection in a receiver circuit (Balraj et al. [0002]).
Claim 16. The BS of claim 14, the combination of Huh et al. and Jeon et al. does not explicitly disclose,
wherein the interference comprises at least one of a co-channel interference or inter-layer interference (ILI).
However, in the related field of endeavor Balraj et al. disclose: … the channel equalized signal is processed on the basis of a first precoding matrix such that the co-channel interference is mitigated [0052]. FIG. 5 all steps.
Therefore, it would have been obvious to a person of ordinary skill in the art, at the time the invention was filed, to modify the teaching of the combination of Huh et al. and Jeon et al. with the teaching of Balraj et al. in order to improve data detection in a receiver circuit (Balraj et al. [0002]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892.
Prior art cited in PTO-892 includes ideas related to the claimed invention. In this regard, Levin et al. (US 2021/0119835 A) disclose the idea of setting the coefficients of equalizers. For example in [0068], Levin et al. disclose: an equalizer setting is determined using a mean-square error of a cost scheme subject to an offset from a minimum cost value. For example, an equalizer can be any of: continuous time linear equalizer (CTLE), decision feedback equalizers (DFE), or feed forward equalizers (FFE). The offset from the minimum value can be set based on a skew in Equation 6. Also, FIG. 6 all the steps.
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MOHAMMED . RACHEDINE
Examiner
Art Unit 2649
/MOHAMMED RACHEDINE/ Primary Examiner, Art Unit 2646
Prosecution Insights