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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/10/2025 has been entered.
Response to Amendment/Remarks
This communication is considered fully responsive to the amendment filed on 12/10/2025.
Claims 1-2, 4-6, 10, 13-14 are pending and are examined in this office action.
No claim has been amended.
No new claim has been added and claims 3, 7-9, 11-12 had been canceled.
Response to Arguments
Applicant’s arguments, filed 12/10/2025 , with respect to the rejection(s) of claim(s) under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of SHEN et al. (US 20220124744 A1; hereinafter as “SHEN”).
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 1-2, 4-5, 10, 13 are rejected under 35 U.S.C. 103 as being unpatentable over SU et al. (US 20180041265 A1; hereinafter as “SU ”) in view of WANG et al. (US 20220014251 A1; hereinafter as “WANG”, which has a foreign priority dated March 27, 2019). And further SHEN et al. (US 20220124744 A1; hereinafter as “SHEN”, which has priority date August 26, 2019).
Examiner’s note: in what follows, references are drawn to SU unless otherwise mentioned.
Regarding claim 1, SU teaches, A user equipment (see fig. 7: A terminal : [0091]; fig. 4: CSI Feedback from Terminal to network device: [0019]; “ FIG. 4 is a flow chart of a CSI feedback method according to one embodiment of the present disclosure;”; [0019]), comprising:
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a receiver (see fig. 9: terminal with transceiver : [0103]) for receiving downlink transmission data including a pilot signal from a base station (see fig. 4 element 401 where network device (==base station in claim ) transmits and aforesaid terminal receives a pilot signal : [0059]; “ In Step 401, the pilot signal received by the terminal is transmitted by the network device through the A antenna ports. … ”: [0065]-[0066] ); an
a processor (==determination unit/channel estimate unit in fig. 7) for encoding the pilot signal into feedback channel state information (see fig. 4: “Based on the pilot signal on the time/frequency resource occupied by each antenna port, the terminal may acquire A channel estimation values corresponding to the A antenna ports for transmitting the pilot signal. The channel estimation channel of the antenna port a may be calculated through the following equation: Ĥ.sup.(a)=Hw.sup.(a)+E.sup.(a) (12), where w.sup.(a) represents the 3D spatial beamforming vector of the antenna port a, E.sup.(a) represents a channel estimation error matrix of the antenna port a, and aεω.sub.A. The terminal may acquire the channel estimation values Ĥ.sup.(1), Ĥ.sup.(2), . . . , and Ĥ.sup.(A) of the A antenna ports. ”: “the terminal needs to select the Q antenna ports from the A antenna ports based on the channel estimation values of the A antenna ports, so as to determine the channel state information (CSI) and feed it back to the network device. ”: [0066]-[0068] ; [0002]); and
a transmitter (see fig. 9: terminal with transceiver : [0103]; fig. 7: transmission unit ) for transmitting the feedback channel state information to the base station (“the terminal needs to select the Q antenna ports from the A antenna ports based on the channel estimation values of the A antenna ports, so as to determine the channel state information (CSI) and feed it back to the network device.’: [0068]; also see fig. 7 element 403 “Step 403: determining, by the terminal, a first-level PMI based on the Q antenna ports, and feeding back CSI containing the first-level PMI to the network device, the first-level PMI being used to indicate indices of the Q antenna ports among the A antenna ports for transmitting the pilot signal. ”: [0062], ; see fig. 1: element 101 “Step 101: receiving, by a network device, CSI feedback from a terminal, the CSI at least including a first-level PMI which is used to indicate indices of Q antenna ports among A antenna ports for transmitting a pilot signal, the first-level PMI being determined based on the Q antenna ports after the terminal has determine the Q antenna ports based on channel estimation values of the A antenna ports, L≦Q≦A, L representing a value of a RI adopted by the network device for transmitting downlink data to the terminal or a value of a RI of a channel determined by the terminal. ”: [0027]),
wherein the base station (==network device) reconstructs a channel matrix of the base station based on the feedback channel state information ( “ determining, by the network device, a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determining, by the network device, a precoding matrix for transmission based on the first-level precoding matrix. ”: [0011]; “determine a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determine a precoding matrix for transmission based on the first-level precoding matrix. The transceiver is configured to receive and transmit data. ”: [0013] ; “The CSI received by the network device may further include one or more of a second-level PMI, a RI and a Channel Quality Indicator (CQI). The second-level PMI is used to indicate an index of a second-level precoding matrix in a second-level codebook set. The second-level precoding matrix is a power-normalized Q*L matrix ”; [0048]; “CSI fed back by the terminal includes the first-level PMI, the number Q of the antenna ports selected by the terminal, the second-level PMI, the RI and the CQI. ”: [0084]).
While SU teaches “a receiver for receiving downlink transmission data including a pilot signal from a base station” , SU appears to be silence on
the pilot signal is an incomplete low-resolution reference signal,
wherein the processor is configured with an encoding neural network, and the encoding neural network comprises at least one full connection layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information.
WANG, in the same field of endeavor, discloses:
“the pilot signal is an incomplete low-resolution reference signal (see fig. 5 where Terminal Device (==User Equipment in Claim ) receives First Indication Information indicating the reporting bandwidth from Network Device : “In operation 420, the network device sends first indication information, where the first indication information is used to indicate the reporting bandwidth ”: [0172]; “ the terminal device may determine the reporting bandwidth based on the first indication information sent by the network device. The first indication information may be, for example, the CSI reporting configuration described above. The CSI reporting configuration may carry an IE csi-ReportingBand, to indicate a frequency domain unit for which a CQI is to be reported. Because a specific indication manner of the reporting bandwidth is described above in detail, for brevity, details are not described herein again.”{0173]; “ network device may map, based on a preconfigured pilot signal, a reference signal to a corresponding RB for transmission, so that the terminal device performs channel measurement based on the reference signal received on the reporting bandwidth. A preconfigured pilot density may be 1, or may be less than 1, for example, 0.5. When the pilot density is 0.5, one RB in every two RBs carries a reference signal. However, if the first or the last subband in the reporting bandwidth is an incomplete subband (==incomplete low-resolution in claim ; see applicant’s OWN specification [0041], ), after the subband is divided based on the second granularity, it is possible that a pilot density in the divided subband is less than the preconfigured pilot density. ”: [0148] ; NOTE: incomplete low-resolution reference signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU to include the above recited limitations as taught by WANG. The suggestion/motivation will provide PMI which is key information for determining the precoding matrix by the network device, a frequency domain granularity for reporting the PMI may be redesigned to obtain a more accurate feedback from the terminal device. (WANG; [0004]).
The combination of SU and WANG does not expressively disclose:
wherein the processor is configured with an encoding neural network, and the encoding neural network comprises at least one full connection layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information.
SHEN , in the same field of endeavor, discloses:
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wherein the processor is configured with an encoding neural network, and the encoding neural network comprises at least one full connection layer for quantizing and compressing the pilot signal into a one-dimensional vector as the feedback channel state information (see fig. 2-3: terminal device with neural network: [0029]-[0030]; terminal device with encoding neural network model: [0051], “ Based on the network architecture of FIG. 13, a method for processing CSI, which is applied to a communication system, is illustrated in FIG. 14. As illustrated in FIG. 14, the network device configures a CSI reference signal (==pilot signal in claim ) for the terminal device, the terminal device performs CSI measurement based on the CSI reference signal to obtain CSI (==feedback channel state information in claim). The terminal device feeds back the CSI indication information type to the network device according to the CSI and feeds back the size of the CSI indication information to the network device. The terminal device uses the CSI as an input of an encoding neural network model, and the encoding neural network model outputs CSI indication information. That is, the terminal device generates CSI indication information based on the CSI with an artificial intelligence (AI) algorithm. The terminal device transmits the CSI indication information to the network device. The network device uses the CSI indication information as the input of a decoding neural network model, and the decoding neural network model outputs CSI. That is, the network device restores the CSI based on the CSI indication information with aid of an AI algorithm. The network device configures a data transmission mode for the terminal device according to the CSI.”:[0096]- [0097]; “ A whole neural network model is constructed through multi-layer information transmission and processing.” : [0029]-[0030]; [0031]-[0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU in view of WANG and LEE to include the above recited limitations as taught by SHEN . The suggestion/motivation will provide effective solution to calculate and improve channel state information (CSI) : (SHEN; [0002]).
Regarding claim 2, SU in view of WANG and SHEN teaches 1 as shown above. Furthermore SU teaches, the user equipment according to claim 1, wherein the pilot signal is the pilot signal whose frequency is controlled by the base station (base station/network device sends Pilot signal to terminal so it has full control of pilot signal: fig. 4 element 401: “ Step 401: performing, by a terminal, channel estimation based on a received pilot signal to acquire channel estimation values of A antenna ports for transmitting the pilot signal by a network device. ”: [0059]).
Regarding claim 4, SU teaches, A base station (see fig. 8: Network Device “As shown in FIG. 8, the present disclosure provides in some embodiments a network device, including a processor 800, a transceiver 810 and a memory 820. The processor 800 is configured to read a program stored in the memory 820, so as to: receive CSI from a terminal, the CSI at least including a first-level PMI which is used to indicate indices of Q antenna ports among A antenna ports for transmitting a pilot signal, ”: [0097] ) comprising:
a transmitter (see fig. 8: transceiver ) for transmitting downlink transmission data including a pilot signal to a user equipment (see fig. 9: terminal/ UE) (see fig. 4 element 401 where network device (==base station in claim ) transmits and aforesaid terminal receives a pilot signal : [0059]; “ In Step 401, the pilot signal received by the terminal is transmitted by the network device through the A antenna ports. … ”: [0065]-[0066])) ;
a receiver (see fig. 8: transceiver ) for receiving uplink transmission data from the user equipment (“the terminal needs to select the Q antenna ports from the A antenna ports based on the channel estimation values of the A antenna ports, so as to determine the channel state information (CSI) and feed it back to the network device.’: [0068]; also see fig. 7 element 403 “Step 403: determining, by the terminal, a first-level PMI based on the Q antenna ports, and feeding back CSI containing the first-level PMI to the network device, the first-level PMI being used to indicate indices of the Q antenna ports among the A antenna ports for transmitting the pilot signal. ”: [0062], ; see fig. 1: element 101 “Step 101: receiving, by a network device, CSI feedback from a terminal, the CSI at least including a first-level PMI which is used to indicate indices of Q antenna ports among A antenna ports for transmitting a pilot signal, the first-level PMI being determined based on the Q antenna ports after the terminal has determine the Q antenna ports based on channel estimation values of the A antenna ports, L≦Q≦A, L representing a value of a RI adopted by the network device for transmitting downlink data to the terminal or a value of a RI of a channel determined by the terminal. ”: [0027] ),
wherein the uplink transmission data includes feedback channel state information (see fig. 4: “Based on the pilot signal on the time/frequency resource occupied by each antenna port, the terminal may acquire A channel estimation values corresponding to the A antenna ports for transmitting the pilot signal. The channel estimation channel of the antenna port a may be calculated through the following equation: Ĥ.sup.(a)=Hw.sup.(a)+E.sup.(a) (12), where w.sup.(a) represents the 3D spatial beamforming vector of the antenna port a, E.sup.(a) represents a channel estimation error matrix of the antenna port a, and aεω.sub.A. The terminal may acquire the channel estimation values Ĥ.sup.(1), Ĥ.sup.(2), . . . , and Ĥ.sup.(A) of the A antenna ports. ”: “the terminal needs to select the Q antenna ports from the A antenna ports based on the channel estimation values of the A antenna ports, so as to determine the channel state information (CSI) and feed it back to the network device. ”: [0066]-[0068] ; [0002]); and a
a processor for decoding the feedback channel state information to obtain a channel matrix of the base station ( “ determining, by the network device, a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determining, by the network device, a precoding matrix for transmission based on the first-level precoding matrix. ”: [0011]; “determine a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determine a precoding matrix for transmission based on the first-level precoding matrix. The transceiver is configured to receive and transmit data. ”: [0013] ; “The CSI received by the network device may further include one or more of a second-level PMI, a RI and a Channel Quality Indicator (CQI). The second-level PMI is used to indicate an index of a second-level precoding matrix in a second-level codebook set. The second-level precoding matrix is a power-normalized Q*L matrix ”; [0048]; “CSI fed back by the terminal includes the first-level PMI, the number Q of the antenna ports selected by the terminal, the second-level PMI, the RI and the CQI. ”: [0084]).
While SU teaches “a transmitter for transmitting downlink transmission data including a pilot signal to a user equipment” , SU appears to be silence on
“the pilot signal is an incomplete low-resolution reference signal”,
wherein the uplink transmission data includes feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector.
WANG, in the same field of endeavor, discloses:
“the pilot signal is an incomplete low-resolution reference signal (see fig. 5 where Terminal Device (==User Equipment in Claim ) receives First Indication Information indicating the reporting bandwidth from Network Device : “In operation 420, the network device sends first indication information, where the first indication information is used to indicate the reporting bandwidth ”: [0172]; “ the terminal device may determine the reporting bandwidth based on the first indication information sent by the network device. The first indication information may be, for example, the CSI reporting configuration described above. The CSI reporting configuration may carry an IE csi-ReportingBand, to indicate a frequency domain unit for which a CQI is to be reported. Because a specific indication manner of the reporting bandwidth is described above in detail, for brevity, details are not described herein again.”{0173]; “ network device may map, based on a preconfigured pilot signal, a reference signal to a corresponding RB for transmission, so that the terminal device performs channel measurement based on the reference signal received on the reporting bandwidth. A preconfigured pilot density may be 1, or may be less than 1, for example, 0.5. When the pilot density is 0.5, one RB in every two RBs carries a reference signal. However, if the first or the last subband in the reporting bandwidth is an incomplete subband (==incomplete low-resolution in claim ; see applicant’s OWN specification [0041], ), after the subband is divided based on the second granularity, it is possible that a pilot density in the divided subband is less than the preconfigured pilot density. ”: [0148] ; NOTE: incomplete low-resolution reference signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU to include the above recited limitations as taught by WANG. The suggestion/motivation will provide PMI which is key information for determining the precoding matrix by the network device, a frequency domain granularity for reporting the PMI may be redesigned to obtain a more accurate feedback from the terminal device. (WANG; [0004]).
The combination of SU and WANG does not expressively disclose:
wherein the uplink transmission data includes feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector.
SHEN , in the same field of endeavor, discloses:
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wherein the uplink transmission data includes feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector (see fig. 2-3: terminal device with neural network: [0029]-[0030]; terminal device with encoding neural network model: [0051], “ Based on the network architecture of FIG. 13, a method for processing CSI, which is applied to a communication system, is illustrated in FIG. 14. As illustrated in FIG. 14, the network device configures a CSI reference signal (==pilot signal in claim ) for the terminal device, the terminal device performs CSI measurement based on the CSI reference signal to obtain CSI (==feedback channel state information in claim). The terminal device feeds back the CSI indication information type to the network device according to the CSI and feeds back the size of the CSI indication information to the network device. The terminal device uses the CSI as an input of an encoding neural network model, and the encoding neural network model outputs CSI indication information. That is, the terminal device generates CSI indication information based on the CSI with an artificial intelligence (AI) algorithm. The terminal device transmits the CSI indication information to the network device. The network device uses the CSI indication information as the input of a decoding neural network model, and the decoding neural network model outputs CSI. That is, the network device restores the CSI based on the CSI indication information with aid of an AI algorithm. The network device configures a data transmission mode for the terminal device according to the CSI.”:[0096]- [0097]; “ A whole neural network model is constructed through multi-layer information transmission and processing.” : [0029]-[0030]; [0031]-[0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU in view of WANG and LEE to include the above recited limitations as taught by SHEN . The suggestion/motivation will provide effective solution to calculate and improve channel state information (CSI) : (SHEN; [0002]).
Regarding claim 5, SU in view of WANG and SHEN teaches 4 as shown above. Furthermore SU teaches, The base station according to claim 4, wherein the transmitter controls frequency of the pilot signal (base station/network device sends Pilot signal to terminal so it has full control of pilot signal: fig. 4 element 401: “ Step 401: performing, by a terminal, channel estimation based on a received pilot signal to acquire channel estimation values of A antenna ports for transmitting the pilot signal by a network device. ”: [0059]).
Regarding claim 10, SU teaches, A channel matrix generation method executed by a base station (see fig. 8: Network Device “As shown in FIG. 8, the present disclosure provides in some embodiments a network device, including a processor 800, a transceiver 810 and a memory 820. The processor 800 is configured to read a program stored in the memory 820, so as to: receive CSI from a terminal, the CSI at least including a first-level PMI which is used to indicate indices of Q antenna ports among A antenna ports for transmitting a pilot signal, ”: [0097] ) , comprising:
transmitting downlink transmission data including a pilot signal to a user equipment (see fig. 9: terminal/ UE) (see fig. 4 element 401 where network device (==base station in claim ) transmits and aforesaid terminal receives a pilot signal : [0059]; “ In Step 401, the pilot signal received by the terminal is transmitted by the network device through the A antenna ports. … ”: [0065]-[0066]));
receiving uplink transmission data from the user equipment, wherein the uplink transmission data comprises feedback channel state information (“the terminal needs to select the Q antenna ports from the A antenna ports based on the channel estimation values of the A antenna ports, so as to determine the channel state information (CSI) and feed it back to the network device.’: [0068]; also see fig. 7 element 403 “Step 403: determining, by the terminal, a first-level PMI based on the Q antenna ports, and feeding back CSI containing the first-level PMI to the network device, the first-level PMI being used to indicate indices of the Q antenna ports among the A antenna ports for transmitting the pilot signal. ”: [0062], ; see fig. 1: element 101 “Step 101: receiving, by a network device, CSI feedback from a terminal, the CSI at least including a first-level PMI which is used to indicate indices of Q antenna ports among A antenna ports for transmitting a pilot signal, the first-level PMI being determined based on the Q antenna ports after the terminal has determine the Q antenna ports based on channel estimation values of the A antenna ports, L≦Q≦A, L representing a value of a RI adopted by the network device for transmitting downlink data to the terminal or a value of a RI of a channel determined by the terminal. ”: [0027] ); and
decoding the feedback channel state information to obtain a channel matrix of the base station ( “ determining, by the network device, a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determining, by the network device, a precoding matrix for transmission based on the first-level precoding matrix. ”: [0011]; “determine a first-level precoding matrix based on the received CSI and beamforming vectors corresponding to the A antenna ports; and determine a precoding matrix for transmission based on the first-level precoding matrix. The transceiver is configured to receive and transmit data. ”: [0013] ; “The CSI received by the network device may further include one or more of a second-level PMI, a RI and a Channel Quality Indicator (CQI). The second-level PMI is used to indicate an index of a second-level precoding matrix in a second-level codebook set. The second-level precoding matrix is a power-normalized Q*L matrix ”; [0048]; “CSI fed back by the terminal includes the first-level PMI, the number Q of the antenna ports selected by the terminal, the second-level PMI, the RI and the CQI. ”: [0084]).
While SU teaches “transmitting downlink transmission data including a pilot signal to a user equipment” , SU appears to be silence on
“the pilot signal is an incomplete low-resolution reference signal”,
wherein the uplink transmission data comprises feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector.
WANG, in the same field of endeavor, discloses:
“the pilot signal is an incomplete low-resolution reference signal (see fig. 5 where Terminal Device (==User Equipment in Claim ) receives First Indication Information indicating the reporting bandwidth from Network Device : “In operation 420, the network device sends first indication information, where the first indication information is used to indicate the reporting bandwidth ”: [0172]; “ the terminal device may determine the reporting bandwidth based on the first indication information sent by the network device. The first indication information may be, for example, the CSI reporting configuration described above. The CSI reporting configuration may carry an IE csi-ReportingBand, to indicate a frequency domain unit for which a CQI is to be reported. Because a specific indication manner of the reporting bandwidth is described above in detail, for brevity, details are not described herein again.”{0173]; “ network device may map, based on a preconfigured pilot signal, a reference signal to a corresponding RB for transmission, so that the terminal device performs channel measurement based on the reference signal received on the reporting bandwidth. A preconfigured pilot density may be 1, or may be less than 1, for example, 0.5. When the pilot density is 0.5, one RB in every two RBs carries a reference signal. However, if the first or the last subband in the reporting bandwidth is an incomplete subband (==incomplete low-resolution in claim ; see applicant’s OWN specification [0041], ), after the subband is divided based on the second granularity, it is possible that a pilot density in the divided subband is less than the preconfigured pilot density. ”: [0148] ; NOTE: incomplete low-resolution reference signal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU to include the above recited limitations as taught by WANG. The suggestion/motivation will provide PMI which is key information for determining the precoding matrix by the network device, a frequency domain granularity for reporting the PMI may be redesigned to obtain a more accurate feedback from the terminal device. (WANG; [0004]).
The combination of SU and WANG does not expressively disclose:
wherein the uplink transmission data comprises feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector.
SHEN , in the same field of endeavor, discloses:
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wherein the uplink transmission data comprises feedback channel state information generated based on quantization and compression of the pilot signal into a one-dimensional vector (see fig. 2-3: terminal device with neural network: [0029]-[0030]; terminal device with encoding neural network model: [0051], “ Based on the network architecture of FIG. 13, a method for processing CSI, which is applied to a communication system, is illustrated in FIG. 14. As illustrated in FIG. 14, the network device configures a CSI reference signal (==pilot signal in claim ) for the terminal device, the terminal device performs CSI measurement based on the CSI reference signal to obtain CSI (==feedback channel state information in claim). The terminal device feeds back the CSI indication information type to the network device according to the CSI and feeds back the size of the CSI indication information to the network device. The terminal device uses the CSI as an input of an encoding neural network model, and the encoding neural network model outputs CSI indication information. That is, the terminal device generates CSI indication information based on the CSI with an artificial intelligence (AI) algorithm. The terminal device transmits the CSI indication information to the network device. The network device uses the CSI indication information as the input of a decoding neural network model, and the decoding neural network model outputs CSI. That is, the network device restores the CSI based on the CSI indication information with aid of an AI algorithm. The network device configures a data transmission mode for the terminal device according to the CSI.”:[0096]- [0097]; “ A whole neural network model is constructed through multi-layer information transmission and processing.” : [0029]-[0030]; [0031]-[0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU in view of WANG and LEE to include the above recited limitations as taught by SHEN . The suggestion/motivation will provide effective solution to calculate and improve channel state information (CSI) : (SHEN; [0002]).
Regarding claim 13, SU in view of WANG and SHEN teaches 10 as shown above. Furthermore SU teaches, The method according to claim 10, further comprising: controlling frequency of the pilot signal (base station/network device sends Pilot signal to terminal so it has full control of pilot signal: fig. 4 element 401: “ Step 401: performing, by a terminal, channel estimation based on a received pilot signal to acquire channel estimation values of A antenna ports for transmitting the pilot signal by a network device. ”: [0059]).
Claims 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over in view of SU in view of WANG and SHEN and Further in view of LEE et al. (US 20210250068 A1; hereinafter as “LEE”, which has a foreign priority dated February 10, 2020).
Examiner’s note: in what follows, references are drawn to SU unless otherwise mentioned.
Regarding claim 6, SU in view of WANG and SHEN teaches claim 4 as shown above.
SU in view of WANG and SHEN does not expressively teaches
The base station according to claim 4 , wherein the processor is configured with a decoding neural network, and the decoding neural network at least comprises a multi-layer residual convolution neural network for super-resolution reconstructing the feedback channel state information into the channel matrix of the base station.
LEE, in the same field of endeavor, discloses:
The base station according to claim 4 , wherein the decoding unit is configured with a decoding neural network, and the decoding neural network at least comprises a multi-layer residual convolution neural network for super-resolution reconstructing the feedback channel state information into the channel matrix of the base station ( “FIG. 8 is a diagram illustrating the respective configurations of a reception device (==user equipment in claim 1) and a transmission device (==base station in claim 1) according to the present disclosure.”: [0138] “[0160] The processor 850 generates a codeword from the feedback information, using at least one of the following: at least one third layer 862, the fourth layer 863, or the quantization layer 864. For example, the codeword is derived from the real part vector and imaginary part vector of the beamforming vector.
[0161] The reception device 800 and the transmission device 850, which are configured as described above, perform the neural network training process to determine the codebook. Through the neural network training process, the beamforming vector or the codeword that corresponds to each piece of feedback information is determined. In addition, the codebook including at least one codeword is determined.
[0162] The codeword or the codebook is derived through the process of training the neural network to maximize the effective channel gain. For the neural network training, the stochastic gradient descent applies.
[0163] When the neural network training is completed, the reception device 800 performs an operation similar to the neural network training. For example, when receiving a signal (for example, a pilot signal) from the transmission device 850, the reception device 800 derives the feedback information from the reception signal vector including the channel component and the noise component, using at least one of the following: at least one first layer 812, the second layer 813, or the quantization layer 814.
[0164] When the neural network training is completed, the transmission device 850 performs an operation of selecting a codeword (or a beamforming vector) corresponding to the feedback information received from the reception device 800, without performing an operation similar to the neural network training. The feedback information and the codeword (or the beamforming vector) have a one-to-one mapping relationship.
”: [0160]-[0164], “ First, the channel information is determined using a signal (for example, a pilot signal) that is preset between the transmitter and the receiver, and the pilot signal received by the receiver from the transmitter is expressed in Equation 2. Equation 2 is an expression that results from adding the received pilot signals in the form of a matrix.
Y.sup.train=√{square root over (E.sub.p)}HP+N Equation 2
[0064] where Y.sup.train denotes a received signal matrix of a pilot, Ep denotes a transmission signal power of a pilot, H denotes a channel matrix, P denotes a transmission signal matrix of a pilot, and N denotes a noise matrix.”: [0063]-[0064]) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU in view of WANG and SHEN to include the above recited limitations as taught by LEE. The suggestion/motivation will provide high-performance feedback from user equipment to network device/eNB (LEE; [0173]).
Regarding claim 14, SU in view of WANG and SHEN teaches claim 10 as shown above.
SU in view of WANG does not expressively teaches
The method station according to claim 10, wherein decoding the feedback channel state information to obtain a channel matrix of the base station comprises: super-resolution reconstructing the feedback channel state information into the channel matrix of the base station with a decoding neural network.
LEE, in the same field of endeavor, discloses:
The method station according to claim 10, wherein decoding the feedback channel state information to obtain a channel matrix of the base station comprises: super-resolution reconstructing the feedback channel state information into the channel matrix of the base station with a decoding neural network ( “FIG. 8 is a diagram illustrating the respective configurations of a reception device (==user equipment in claim 1) and a transmission device (==base station in claim 1) according to the present disclosure.”: [0138] “[0160] The processor 850 generates a codeword from the feedback information, using at least one of the following: at least one third layer 862, the fourth layer 863, or the quantization layer 864. For example, the codeword is derived from the real part vector and imaginary part vector of the beamforming vector.
[0161] The reception device 800 and the transmission device 850, which are configured as described above, perform the neural network training process to determine the codebook. Through the neural network training process, the beamforming vector or the codeword that corresponds to each piece of feedback information is determined. In addition, the codebook including at least one codeword is determined.
[0162] The codeword or the codebook is derived through the process of training the neural network to maximize the effective channel gain. For the neural network training, the stochastic gradient descent applies.
[0163] When the neural network training is completed, the reception device 800 performs an operation similar to the neural network training. For example, when receiving a signal (for example, a pilot signal) from the transmission device 850, the reception device 800 derives the feedback information from the reception signal vector including the channel component and the noise component, using at least one of the following: at least one first layer 812, the second layer 813, or the quantization layer 814.
[0164] When the neural network training is completed, the transmission device 850 performs an operation of selecting a codeword (or a beamforming vector) corresponding to the feedback information received from the reception device 800, without performing an operation similar to the neural network training. The feedback information and the codeword (or the beamforming vector) have a one-to-one mapping relationship.
”: [0160]-[0164], “ First, the channel information is determined using a signal (for example, a pilot signal) that is preset between the transmitter and the receiver, and the pilot signal received by the receiver from the transmitter is expressed in Equation 2. Equation 2 is an expression that results from adding the received pilot signals in the form of a matrix.
Y.sup.train=√{square root over (E.sub.p)}HP+N Equation 2
[0064] where Y.sup.train denotes a received signal matrix of a pilot, Ep denotes a transmission signal power of a pilot, H denotes a channel matrix, P denotes a transmission signal matrix of a pilot, and N denotes a noise matrix.”: [0063]-[0064]) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of SU in view of WANG and SHEN to include the above recited limitations as taught by LEE. The suggestion/motivation will provide high-performance feedback from user equipment to network device/eNB (LEE: [0173]).
Conclusion
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/M Mostazir Rahman/Examiner, Art Unit 2411
/DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411