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 .
Applicant’s election without traverse of Group I: Claims 1-5 and 16-20, drawn to a user equipment (UE) and a processor for power control and interference coordination in wireless communication is acknowledged.
Claims 1-5 and 16-20 are subject to examination and rejected.
Allowable Subject Matter
Claims 4, 5, 19 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Claims 1-3 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US PGPub 2006/0281462) in view of He (Yuan He et al., "Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network," in IEEE Communications Letters, vol. 24, no. 10, pp. 2221-2225, Oct. 2022, publication date 6/30/2020).
Regarding claims 1 and 16, Kim teaches a user equipment (UE) for wireless communication (Kim, see figure 1, a Mobile Station), comprising:
at least one memory (Kim, see figure 1, a Mobile Station); and
at least one processor coupled with the at least one memory and configured to cause the UE to (Kim, see figure 1, a Mobile Station):
receive a pilot signal from a first number of first base stations (BSs) (Kim, see paragraph 0010, the mobile station receives pilot signals from the serving base station and neighbor base stations).
Kim teaches the above yet fails to teach generate a serving BS matrix, wherein the serving BS matrix indicates that the UE accesses a second number of first BSs among the first number of first BSs; measure channel state information (CSI) between the UE and each of the first number of first BSs; generate a CSI matrix based on the measured CSI between the UE and the first number of first BSs; encode the serving BS matrix and the CSI matrix; and transmit the encoded serving BS matrix and the encoded CSI matrix to one of the second number of first BSs.
Then He teaches generate a serving BS matrix (He, see section II. A. system model on page 2221, the association matrix of BSs and users with binary element (0 or 1)), wherein the serving BS matrix indicates that the UE accesses a second number of first BSs among the first number of first BSs (He, see section II. B. architecture of deep residual network on page 2223, the association matrices contain the cluster information of which BSs serve users);
measure channel state information (CSI) between the UE and each of the first number of first BSs (He, see section II. A. system model on page 2221, Let Hi = [Hi,1,Hi,2,...,Hi,J] ∈ CN× MJ represents the overall CSI from all BSs to the user i);
generate a CSI matrix based on the measured CSI between the UE and the first number of first BSs (He, see section II. A. system model on page 2221, where Hi,k ∈ CN× M denotes the channel matrix between user i and BS k.);
encode the serving BS matrix and the CSI matrix (He, see the comment of figure 1 on page 2223, 1) Subnet-1, the real and imaginary parts (R&I) of CSI is input to a residual network; 2) Subnet-2, the magnitude and phase parts (M&P) of CSI is input to the same residual network; 3) Subnet-3, the association matrices are input to multiple fully-connected layers); and
transmit the encoded serving BS matrix and the encoded CSI matrix to one of the second number of first BSs (He, see the comment of figure 1 on page 2223, Data fusion module, the output of three subnets are fused via DNN and output the beamforming solutions through specific activation functions.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim with Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network of He, because doing so would make Kim more efficient in improving the performance of approximator in UCN by introducing a deep residual learning framework with three subnets (He, see section II. B. architecture of deep residual network on page 2223).
Regarding claims 2 and 17, Kim in view of He teaches wherein the serving BS matrix comprises a first number of elements, each of which corresponds to a respective one of the first number of first BSs (He, see section II. A. system model on page 2221, the association matrix of BSs and users with binary element (0 or 1)), and wherein an element of the serving BS matrix being a first value indicates that a corresponding first BS is a serving BS of the UE, or the element of the serving BS matrix being a second value indicates that the corresponding first BS is not the serving BS of the UE (He, see section II. B. architecture of deep residual network on page 2223, the association matrices contain the cluster information of which BSs serve users).
Regarding claims 3 and 18, Kim in view of He teaches wherein the CSI matrix comprises at least one of:
a first matrix of channel amplitude information and a second matrix of channel phase information; or a third matrix of a real part associated with channel fading and a fourth matrix of an imagery part associated with the channel fading (He, see the comment of figure 1 on page 2223, 1) Subnet-1, the real and imaginary parts (R&I) of CSI is input to a residual network; 2) Subnet-2, the magnitude and phase parts (M&P) of CSI is input to the same residual network).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHONG G KIM whose telephone number is (571)270-0619. The examiner can normally be reached Mon-Fri @ 9am - 5pm.
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/CHONG G KIM/Examiner, Art Unit 2443
/CHRISTOPHER B ROBINSON/Primary Examiner, Art Unit 2443
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