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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed on June 8, 2024, and November 26, 2025. U.S. patent applications, foreign patents, and non-patent literature documents have been considered.
Priority
Applicant cannot rely upon the certified copy of the foreign priority application to overcome this rejection because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216.
Drawings
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Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-4, and 8-13 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Jin et. al. (CN 110350958 A), herein referred to as “Jin”. This reference is provided by the information disclosure sheet dated June 8, 2024.
Regarding Claim 1,
Jin discloses: A feedback method for channel state information applied to a terminal, comprising:
compressing first channel state information with M compression multiples by an encoder, to obtain M groups of second channel state information, where M is a positive integer and not less than 2
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. There are different encoders that provide different levels of compression (4, 8, 16, and 32), thus being a “multiple” of the encoders. Each of these encoders then outputs the compressed CSI information (“group of second channel state information”). Therefore the encoders generate four groups of second channel state information (i.e., M = 4).
and feeding back the M groups of second channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. The transmission from the UE/encoder to the BS/decoder corresponds to the “feeding back . . . groups of second channel state information”, where the encoder allows for the different compressed CSI groups as aforementioned to be fed back to the base station.
Regarding Claim 2,
Jin discloses: The feedback method according to claim 1, wherein the encoder comprises N compression sub-modules, where N is a positive integer not less than 2, and in the operation of compressing the first channel state information with M compression multiples by the encoder, a plurality of the N compression sub-modules are selected to perform the compression with M compression multiples on the first channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. The encoder has one compression encoder with a compression rate of 4 times (“CR: 4”) and three 2x compression encoders, which each of them constitute N= 4 compression sub-modules which are selected according to the required target compression rate.
Regarding Claim 3,
Jin discloses: The feedback method according to claim 2, wherein 2≤M≤N, the N compression sub-modules have compression multiples different from each other, and in the operation of compressing the first channel state information with M compression multiples by the encoder, M of the N compression sub-modules are selected to perform the compression with M compression multiples on the first channel state information.
[0003] At present, in the research of large-scale MIMO system channel state information feedback by using space theory of relativity of channel state information and compressed sensing can be the channel state information to the sparse matrix under certain base, then using compressed sensing to obtain low-dimension measuring value, transmitting to the base station end through the feedback link, the base station end reconstructing original sparse channel matrix from the measured value. based on compressed sensing method provides reconstruction model CsiNet based on channel feedback and deep learning as a non-iterative algorithm, which greatly reduces the feedback overhead. The model based on automatic encoder architecture, the user end of the coding, decoding and reconstruction at the base station end. Because the whole network is a "black box", so the network is configured not with principle fully uses CSI encoding and decoding, on the design of the network at the place needed to be improved. In addition the condition CSI feedback of large-scale MIMO system the short coherence time of the lower needs to be greatly compressed, when the coherence time is long can be compressed with small amplitude. Therefore, the compression rate of the CSI must can be adjusted according to the environment. iterative algorithm can be applied to different compression ratios, but the existing compression method based on deep learning lower can only at a fixed compression rate compression of CSI matrix, a user needs to store a plurality of network architecture and the corresponding parameter set can realize variable rate compression, due to the limited storage space of the user end, this is not feasible.
Note: Referring to Figure 2 that shows concatenated blocks providing different compression rates (4, 8, 16, and 32), each of the blocks can be considered to be a sub-module. However, each processing block itself does not have a different compression rate, since the “CR: 8” is obtained by three blocks having a 2x compression rate, which is also applied to CR: 16 and CR: 32. Since the decoder shows four separate decoder entities with different compression rates, it would also be the same structure in the encoder. Paragraph [0003] then demonstrates that at least one transition in the coherence time would require CSI compression rates CR: 4, CR:8, CR: 16, and CR: 32 and would involve at least 2 encoder entities.
Regarding Claim 4,
Jin discloses: The feedback method according to claim 2, wherein in the operation of compressing the first channel state information with M compression multiples by the encoder, compression with any one of the M compression multiples is implemented by any one or combination of the compression sub-modules, wherein a compression operation with the smallest compression multiple uses the smallest number of compression sub-modules, while a compression operation with the largest compression multiple uses the largest number of compression sub-modules.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: The combination is achieved by the concatenation of different encoders to different rates, where the smallest compression rate (CR: 4) uses a single compression entity (CR: 4 encoder), and the largest compression rate (CR: 32) uses four compression entities (4, 2x, 2x, 2x).
Regarding Claim 8,
Jin discloses: The feedback method according to claim 2, wherein each compression sub-module satisfies any one of the following conditions: the compression sub-module comprises a fully-connected network comprising at least two fully-connected layers; the compression sub-module comprises a convolutional network comprising at least one of: at least one convolutional layer, at least one residual block, at least one dense block, at least one pooling layer, or at least one direct layer; or the compression sub-module comprises a recurrent neural network comprising a long short term memory (LSTM) or a gated recurrent unit (GRU).
[0013] 1) CsiNet + of the encoder used in the two 7 * 7 convolution layer to replace CsiNet at the encoder of the first 3 * 3 convolution layer of the decoder CsiNet +, RefineNet respectively by a 7 * 7 a 5 * 5 convolution kernel to replace CsiNet at the corresponding position of the two 3 * 3 convolution kernel.
Regarding Claim 9,
Jin discloses: The feedback method according to claim 1, wherein in the operation of feeding back each group of second channel state information, the group of second channel state information is fed back periodically, and a length of a feedback period is negatively correlated with the compression multiple corresponding to the second channel state information fed back within the period, and for the same compression multiple and different feedback periods, the first channel state information obtained in real time is compressed.
[0003] At present, in the research of large-scale MIMO system channel state information feedback by using space theory of relativity of channel state information and compressed sensing can be the channel state information to the sparse matrix under certain base, then using compressed sensing to obtain low-dimension measuring value, transmitting to the base station end through the feedback link, the base station end reconstructing original sparse channel matrix from the measured value. based on compressed sensing method provides reconstruction model CsiNet based on channel feedback and deep learning as a non-iterative algorithm, which greatly reduces the feedback overhead. The model based on automatic encoder architecture, the user end of the coding, decoding and reconstruction at the base station end. Because the whole network is a "black box", so the network is configured not with principle fully uses CSI encoding and decoding, on the design of the network at the place needed to be improved. In addition the condition CSI feedback of large-scale MIMO system the short coherence time of the lower needs to be greatly compressed, when the coherence time is long can be compressed with small amplitude. Therefore, the compression rate of the CSI must can be adjusted according to the environment. iterative algorithm can be applied to different compression ratios, but the existing compression method based on deep learning lower can only at a fixed compression rate compression of CSI matrix, a user needs to store a plurality of network architecture and the corresponding parameter set can realize variable rate compression, due to the limited storage space of the user end, this is not feasible.
Note: CSI is reported more frequently for shorter coherence times, which results in the compression rate and the feedback period are negatively correlated.
Regarding Claim 10,
Jin discloses: The feedback method according to claim 9, wherein the feedback method further comprises: feeding back the length of the feedback period for each group of second channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. The transmission from the UE/encoder to the BS/decoder corresponds to the “feeding back . . . groups of second channel state information”, where the encoder allows for the different compressed CSI groups as aforementioned to be fed back to the base station. Since multi-rate compression encoder is in frames, the “frames” can be interpreted as the period.
Regarding Claim 11,
Jin discloses: The feedback method according to claim 1, wherein before compressing first channel state information with M compression multiples by the encoder, the feedback method further comprises: acquiring original channel information; performing feature extraction on the original channel information by the encoder to obtain initial channel state information; and pre-processing the initial channel state information to obtain the first channel state information.
[0042] (2) introducing the series multi-rate compressed frame SM-CsiNet +, the compression encoder different rates of connected in series at the user end, for the compressed code word of small magnification encoder to generate the compressed code word of high rate encoder, at the same time, before compression for two convolutional layer before extracting the feature of also shared by different compression encoder, the output series of the different decoder at the base station end, for end to end of the training; it realizes the number variable rate compression and greatly reduce the encoder parameters.
Regarding Claim 12,
Jin discloses: The feedback method according to claim 11, wherein the encoder further comprises a first processing module configured to perform the feature extraction on the original channel information, and the first processing module satisfies any one of the following conditions: the first processing module comprises a fully-connected network comprising at least two fully-connected layers; the first processing module comprises a convolutional network comprising at least one of: at least one convolutional layer, at least one residual block, at least one dense block, at least one pooling layer, or at least one direct layer; or the first processing module comprises a recurrent neural network comprising a long short term memory (LSTM) or a gated recurrent unit (GRU).
[0013] 1) CsiNet + of the encoder used in the two 7 * 7 convolution layer to replace CsiNet at the encoder of the first 3 * 3 convolution layer of the decoder CsiNet +, RefineNet respectively by a 7 * 7 a 5 * 5 convolution kernel to replace CsiNet at the corresponding position of the two 3 * 3 convolution kernel.
Regarding Claim 13,
Jin discloses: The feedback method according to claim 12, wherein in the operation of pre-processing the initial channel state information to obtain the first channel state information, the first channel state information has a dimension consistent with an input dimension of the encoder.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Since “input dimension” is not clearly defined in the claim, it is being broadly interpreted as the one-to-one relationship between encoder entities and the compression rate.
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 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of Pezeshki et. al. (U.S. Pat. Pub. 2021/0195462), herein referred to as “Pezeshski”. The Pezeshski reference was provided in the information disclosure statement dated November 26, 2025.
Regarding Claim 5,
Jin dies not disclose all the limitations of Claim 5.
However, Pezeshki discloses: The feedback method according to claim 4, wherein the feedback method further comprises: feeding back identification information of compression sub-modules for achieving each of the compression multiples, or feeding back identification information of one or more of the plurality of compression sub-modules, which satisfy a preset condition, for achieving each of the compression multiples.
[0060] In certain aspects, the configuration 510 may be an indication of a compression ratio to be used for the compression of the one or more measurements corresponding to the reference signal 506, an indication of at least one AI module (one of AI module(s) 512) to be used for the compression of the one or more measurements corresponding to the reference signal 506, or both.
[0062] In some cases, the configuration 510 indicated by the BS may be an index associated with one of the AI modules 512, which may be configured with a specific compression ratio. In other words, multiple AI modules may be associated with different indices and correspond to different compression ratios, and the configuration 510 may indicate one of the indices associated with the AI modules.
Note: Per Figure 5, since the BS controls the encoding procedure by providing the encoding configuration to the UE, the UE can also feedback the information to the BS. The “preset condition” is where the encoders correspond to the required (specific) compression ratio.
Jin and Pezeshki are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of a preset condition with compression sub-modules as taught by Pezeshki so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 6,
Jin dies not disclose all the limitations of Claim 6.
However, Pezeshki discloses: The feedback method according to claim 5, wherein the N compression sub-modules are connected in series and output independently of each other, the identification information of each compression sub-module comprises a sequence number of the compression sub-module, the N compression sub-modules are numbered according to a first preset rule, and in the operation of feeding back the identification information of one or more of the plurality of compression sub-modules, which satisfy the preset condition, for achieving each of the compression multiples, at least the identification information of the compression sub-module that outputs each group of second channel state information is fed back.
[0064] In some cases, the UE may have different classes of AI modules, each class of AI modules configured for compressing different types of signals (e.g., reference signals), or configured to compress signals for different purposes (e.g., calculation of CQI or PMI). For example, one AI module may be trained for compression of CSI measurements, whereas another AI module may be trained for different purposes. Thus, the BS may indicate the class of AI modules that the UE is to use. In some aspects, the BS may indicate an index associated with a specific AI module to be used for compression and generation of the codeword 514. The BS may determine the index associated with the specific AI module based on a type or quantity of parameters to be calculated at the BS. For example, the greater the quantity of the one or more parameters, the lower the compression ratio to be selected may be for generating the codeword.
Note: Jin discloses the compression module compression as previously discussed (Figure 2). Since there is a one-to-one relationship between encoder entities and the compression rate, providing the compression rate automatically provides the corresponding compression entity identification. The “preset condition” is in terms of the identification of AI modules.
Jin and Pezeshki are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of a preset condition with compression sub-modules as taught by Pezeshki so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 7,
Jin dies not disclose all the limitations of Claim 7.
However, Pezeshki discloses: The feedback method according to claim 6, wherein the first preset rule comprises numbering the N compression sub-modules consecutively in increments from 1 to N, and the sequence number of each compression sub-module is positively correlated with a compression multiple output by the compression sub-module, and the preset condition comprises the largest one of the sequence numbers of the plurality of compression sub-modules for achieving each compression multiple; or the first preset rule comprises numbering the N compression sub-modules consecutively in decrements from N to 1, and the sequence number of each compression sub-module is negatively correlated with the compression multiple of the compression sub-module, and the preset condition comprises the smallest one of the sequence numbers of the plurality of compression sub-modules for achieving each compression multiple.
[0064] In some cases, the UE may have different classes of AI modules, each class of AI modules configured for compressing different types of signals (e.g., reference signals), or configured to compress signals for different purposes (e.g., calculation of CQI or PMI). For example, one AI module may be trained for compression of CSI measurements, whereas another AI module may be trained for different purposes. Thus, the BS may indicate the class of AI modules that the UE is to use. In some aspects, the BS may indicate an index associated with a specific AI module to be used for compression and generation of the codeword 514. The BS may determine the index associated with the specific AI module based on a type or quantity of parameters to be calculated at the BS. For example, the greater the quantity of the one or more parameters, the lower the compression ratio to be selected may be for generating the codeword.
Note: Similar to Claim 6, Jin discloses the compression module compression as previously discussed (Figure 2). Since there is a one-to-one relationship between encoder entities and the compression rate, providing the compression rate automatically provides the corresponding compression entity identification. The “preset condition” is in terms of the identification of AI modules which is in index form, thus allowing for the identification to be ordered in consecutively.
Jin and Pezeshki are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of a preset condition with compression sub-modules as taught by Pezeshki so as to acquire more accurate CSI in multi-antenna technology.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of Song and Yang, “Machine Learning Enhanced CSI Acquisition and Training Strategy for FDD Massive MIMO”, 2021 IEEE Wireless Communications and Networking Conference, March 29, 2021, herein referred to as “Song.” The Song reference was provided in the information disclosure statement dated November 26, 2025.
Regarding Claim 14,
Jin dies not disclose all the limitations of Claim 14.
However, Song discloses: The feedback method according to claim 1, wherein the feedback method further comprises: feeding back compression multiples corresponding to the M groups of second channel state information.
(Page 3) Mode 0: UE applies "static" beamspace compression that matches its array response in the range of interest.
Mode 1: One way to compress the CSI is to apply the dynamic and channel dependent scheme, i.e., based on the estimated channel. UE figures out the significant components and aligns them with the pre-defined codebook (such as DFT codebook). This scheme ensures that the UE selects the compression matrix from the codebook to maximize the effective array gain during the reception, which is dynamic and CST dependent.
Please note that the difference between Mode 0 and Mode l lies in whether it is the quasi-static design of the compression or not as well as different inputs and NNs. Furthermore, UE's implementations for two modes are also different.
If Mode l is indicated, the BS needs to firstly carries out a Pre-Calculation (PreCal) on the CSI feedback H(k) to pre-reconstruct the CSL since UE applies dynamic compression which depends on the channel. The BS implements Neural Network for Mode 1 (NN1) to recover the CSI.
Note: Please refer to Figure 3. For the recovery of the compressed CSI of Mode 1, the BS requires the UE to feedback to the compression matric, which conveys the compression rate.
Jin and Song are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of feeding back compression multiples as taught by Song so as to acquire more accurate CSI in multi-antenna technology.
Claim 15-17, 19, 33, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of 3GPP TSG RAN WG1 #76bis, “Remaining Issues for P-CSI Reporting”, R1-141271, March 31 – April 4, 2014, herein referred to as “R1-141271”. The R1-141271 reference was provided in the information disclosure statement dated November 26, 2025.
Regarding Claim 15,
Jin discloses: An acquisition method for channel state information applied to a base station, comprising: receiving M groups of second channel state information, where each group of the received second channel state information is information obtained by compressing first channel state information with a corresponding multiple by an encoder, where M is a positive integer and not less than 2
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. There are different encoders that provide different levels of compression (4, 8, 16, and 32), thus being a “multiple” of the encoders. Each of these encoders then outputs the compressed CSI information (“group of second channel state information”). Therefore the encoders generate four groups of second channel state information (i.e., M = 4).
decompressing and
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. Each of the decoders (CR: 4, CR: 8, CR: 16, and CR: 32) decompress a group of second CSI from which a target channel state is obtained.
wherein one group of second channel state information with the smallest data amount is decompressed with an expansion multiple greater than other groups of second channel state information, and the target channel state information has the same number of elements as the first channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: For the decoder/encoder construct to function (Figure 2), the reconstructed “target” after decompression in the receiver end must correspond to the original CSI compressed at the transmitter, therefore both the target and the first CSI have the same content.
Jin does not disclose combining the respective groups of second channel state information.
However, R1-141271 discloses combining the respective groups of second channel state information.
(Section 2, pages 1-2) 2 P-CSI Reporting Aspects
Dropping the P-CSI report is not a viable option for several reasons. A first reason is that a P-CSI transmission can coincide with a PUSCH transmission and then it is not possible for the eNB to detect that the UE did not transmit P-CSI (similar to an eNB not implementing PUCCH Format 2 DTX detection). For example, as TDD DL-UL configuration 5 is a likely DL reference configuration, UEs with SPS PUSCH will be transmitting both P-CSI and SPS PUSCH in SF#2.
Even if PUCCH Format 2 DTX detection is supported by an eNB, the eNB cannot infer anything regarding whether the UE missed the DCI format IC with the configured eIMTA-RNTI from a P-CSI DTX detection as there is always the possibility of an incorrect one (the false alarm rate defined in TS 36.104 is 10%). A second reason is that PUCCH Format 2 DTX detection is an optional feature for an eNB and although a UE will not be receiving PDSCHs in DL "flexible" SFs when it operates under the fallback TDD UL-DL configuration, an eNB typically averages P-CSI reports and receiving random ones (due to absence of DTX detection when P-CSI is dropped by the UE) is clearly detrimental.
Note: “Typically averages” is being interpreted as a combination.
Jin and R1-141271 are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of combining the respective groups of second channel state information as taught by R1-141271 so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 16,
Jin discloses: The acquisition method according to claim 15, wherein the decoder comprises N decompression sub-modules, where N is a positive integer not less than 2, and decompressing and combining the respective groups of second channel state information comprises:
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. As stated in Claim 2, the encoder has one compression encoder with a compression rate of 4 times (“CR: 4”) and three 2x compression encoders, which each of them constitute N= 4 compression sub-modules which are selected according to the required target compression rate. However, the decompression also correlates directly to the encoders.
determining a plurality of the N decompression sub-modules corresponding to the M groups of second channel state information; and
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
decompressing and
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: Refer to Figure 2 of this reference. Each of the decoders (CR: 4, CR: 8, CR: 16, and CR: 32) decompress a group of second CSI from which a target channel state is obtained.
wherein one group of second channel state information with the smallest data amount is decompressed with an expansion multiple greater than other groups of second channel state information, and the target channel state information has the same number of elements as the first channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: For the decoder/encoder construct to function (Figure 2), the reconstructed “target” after decompression in the receiver end must correspond to the original CSI compressed at the transmitter, therefore both the target and the first CSI have the same content.
Jin does not disclose combining the groups of second channel state information.
However, R1-141271 discloses combining the groups of second channel state information.
(Section 2, pages 1-2) 2 P-CSI Reporting Aspects
Dropping the P-CSI report is not a viable option for several reasons. A first reason is that a P-CSI transmission can coincide with a PUSCH transmission and then it is not possible for the eNB to detect that the UE did not transmit P-CSI (similar to an eNB not implementing PUCCH Format 2 DTX detection). For example, as TDD DL-UL configuration 5 is a likely DL reference configuration, UEs with SPS PUSCH will be transmitting both P-CSI and SPS PUSCH in SF#2.
Even if PUCCH Format 2 DTX detection is supported by an eNB, the eNB cannot infer anything regarding whether the UE missed the DCI format IC with the configured eIMTA-RNTI from a P-CSI DTX detection as there is always the possibility of an incorrect one (the false alarm rate defined in TS 36.104 is 10%). A second reason is that PUCCH Format 2 DTX detection is an optional feature for an eNB and although a UE will not be receiving PDSCHs in DL "flexible" SFs when it operates under the fallback TDD UL-DL configuration, an eNB typically averages P-CSI reports and receiving random ones (due to absence of DTX detection when P-CSI is dropped by the UE) is clearly detrimental.
Note: “Typically averages” is being interpreted as a combination.
Jin and R1-141271 are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of combining the groups of second channel state information as taught by R1-141271 so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 17,
Jin discloses: The acquisition method according to claim 16, wherein 2≤M≤N, the N decompression sub-modules have expansion multiples different from each other, and in the operation of determining the plurality of the N decompression sub-modules corresponding to the M groups of second channel state information, M of the N decompression sub-modules corresponding to the M groups of second channel state information are determined, and
[0003] At present, in the research of large-scale MIMO system channel state information feedback by using space theory of relativity of channel state information and compressed sensing can be the channel state information to the sparse matrix under certain base, then using compressed sensing to obtain low-dimension measuring value, transmitting to the base station end through the feedback link, the base station end reconstructing original sparse channel matrix from the measured value. based on compressed sensing method provides reconstruction model CsiNet based on channel feedback and deep learning as a non-iterative algorithm, which greatly reduces the feedback overhead. The model based on automatic encoder architecture, the user end of the coding, decoding and reconstruction at the base station end. Because the whole network is a "black box", so the network is configured not with principle fully uses CSI encoding and decoding, on the design of the network at the place needed to be improved. In addition the condition CSI feedback of large-scale MIMO system the short coherence time of the lower needs to be greatly compressed, when the coherence time is long can be compressed with small amplitude. Therefore, the compression rate of the CSI must can be adjusted according to the environment. iterative algorithm can be applied to different compression ratios, but the existing compression method based on deep learning lower can only at a fixed compression rate compression of CSI matrix, a user needs to store a plurality of network architecture and the corresponding parameter set can realize variable rate compression, due to the limited storage space of the user end, this is not feasible.
decompressing and
decompressing the M groups of second channel state information by M decompression sub-modules respectively to obtain M groups of third channel state information;
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
and
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Jin does not disclose combining the groups of third channel state information.
However, R1-141271 discloses combining the groups of third channel state information.
(Section 2, pages 1-2) 2 P-CSI Reporting Aspects
Dropping the P-CSI report is not a viable option for several reasons. A first reason is that a P-CSI transmission can coincide with a PUSCH transmission and then it is not possible for the eNB to detect that the UE did not transmit P-CSI (similar to an eNB not implementing PUCCH Format 2 DTX detection). For example, as TDD DL-UL configuration 5 is a likely DL reference configuration, UEs with SPS PUSCH will be transmitting both P-CSI and SPS PUSCH in SF#2.
Even if PUCCH Format 2 DTX detection is supported by an eNB, the eNB cannot infer anything regarding whether the UE missed the DCI format IC with the configured eIMTA-RNTI from a P-CSI DTX detection as there is always the possibility of an incorrect one (the false alarm rate defined in TS 36.104 is 10%). A second reason is that PUCCH Format 2 DTX detection is an optional feature for an eNB and although a UE will not be receiving PDSCHs in DL "flexible" SFs when it operates under the fallback TDD UL-DL configuration, an eNB typically averages P-CSI reports and receiving random ones (due to absence of DTX detection when P-CSI is dropped by the UE) is clearly detrimental.
Note: “Typically averages” is being interpreted as a combination.
Jin and R1-141271 are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of combining the groups of channel state information as taught by R1-141271 so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 19,
Jin discloses: The acquisition method according to claim 16, wherein in the operation of determining the plurality of the N decompression sub-modules corresponding to the M groups of second channel state information, each group of second channel state information corresponds to at least one decompression sub-module, and the second channel state information with a greater compression multiple corresponding to more decompression sub-modules; and
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
decompressing and
decompressing the second channel state information other than the second channel state information with the smallest compression multiple by corresponding ones of a plurality of decompression sub-modules, to obtain M−1 groups of first intermediate channel state information, wherein the first intermediate channel state information has the same number of elements as the second channel state information with the smallest compression multiple;
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
decompressing the second intermediate channel state information by a plurality of decompression sub-modules to obtain the target channel state information.
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Note: For the decoder/encoder construct to function (Figure 2), the reconstructed “target” after decompression in the receiver end must correspond to the original CSI compressed at the transmitter, therefore both the target and the first CSI have the same content.
Jin does not disclose combining the groups of channel state information.
However, R1-141271 discloses combining the groups of channel state information.
(Section 2, pages 1-2) 2 P-CSI Reporting Aspects
Dropping the P-CSI report is not a viable option for several reasons. A first reason is that a P-CSI transmission can coincide with a PUSCH transmission and then it is not possible for the eNB to detect that the UE did not transmit P-CSI (similar to an eNB not implementing PUCCH Format 2 DTX detection). For example, as TDD DL-UL configuration 5 is a likely DL reference configuration, UEs with SPS PUSCH will be transmitting both P-CSI and SPS PUSCH in SF#2.
Even if PUCCH Format 2 DTX detection is supported by an eNB, the eNB cannot infer anything regarding whether the UE missed the DCI format IC with the configured eIMTA-RNTI from a P-CSI DTX detection as there is always the possibility of an incorrect one (the false alarm rate defined in TS 36.104 is 10%). A second reason is that PUCCH Format 2 DTX detection is an optional feature for an eNB and although a UE will not be receiving PDSCHs in DL "flexible" SFs when it operates under the fallback TDD UL-DL configuration, an eNB typically averages P-CSI reports and receiving random ones (due to absence of DTX detection when P-CSI is dropped by the UE) is clearly detrimental.
Note: “Typically averages” is being interpreted as a combination.
Jin and R1-141271 are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of combining the groups of channel state information as taught by R1-141271 so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 33,
Jin discloses: The acquisition method according to claim 15, wherein the acquisition method further comprises: receiving information on a length of a feedback period of each group of second channel state information, wherein the feedback period of the second channel state information is negatively correlated with a data amount of the second channel state information
[0003] At present, in the research of large-scale MIMO system channel state information feedback by using space theory of relativity of channel state information and compressed sensing can be the channel state information to the sparse matrix under certain base, then using compressed sensing to obtain low-dimension measuring value, transmitting to the base station end through the feedback link, the base station end reconstructing original sparse channel matrix from the measured value. based on compressed sensing method provides reconstruction model CsiNet based on channel feedback and deep learning as a non-iterative algorithm, which greatly reduces the feedback overhead. The model based on automatic encoder architecture, the user end of the coding, decoding and reconstruction at the base station end. Because the whole network is a "black box", so the network is configured not with principle fully uses CSI encoding and decoding, on the design of the network at the place needed to be improved. In addition the condition CSI feedback of large-scale MIMO system the short coherence time of the lower needs to be greatly compressed, when the coherence time is long can be compressed with small amplitude. Therefore, the compression rate of the CSI must can be adjusted according to the environment. iterative algorithm can be applied to different compression ratios, but the existing compression method based on deep learning lower can only at a fixed compression rate compression of CSI matrix, a user needs to store a plurality of network architecture and the corresponding parameter set can realize variable rate compression, due to the limited storage space of the user end, this is not feasible.
Note: CSI is reported more frequently for shorter coherence times, which results in the compression rate and the feedback period are negatively correlated. Also, please note that, via Figure 2, decompression also occurs in the disclosure.
in the operation of decompressing and
[0003] At present, in the research of large-scale MIMO system channel state information feedback by using space theory of relativity of channel state information and compressed sensing can be the channel state information to the sparse matrix under certain base, then using compressed sensing to obtain low-dimension measuring value, transmitting to the base station end through the feedback link, the base station end reconstructing original sparse channel matrix from the measured value. based on compressed sensing method provides reconstruction model CsiNet based on channel feedback and deep learning as a non-iterative algorithm, which greatly reduces the feedback overhead. The model based on automatic encoder architecture, the user end of the coding, decoding and reconstruction at the base station end. Because the whole network is a "black box", so the network is configured not with principle fully uses CSI encoding and decoding, on the design of the network at the place needed to be improved. In addition the condition CSI feedback of large-scale MIMO system the short coherence time of the lower needs to be greatly compressed, when the coherence time is long can be compressed with small amplitude. Therefore, the compression rate of the CSI must can be adjusted according to the environment. iterative algorithm can be applied to different compression ratios, but the existing compression method based on deep learning lower can only at a fixed compression rate compression of CSI matrix, a user needs to store a plurality of network architecture and the corresponding parameter set can realize variable rate compression, due to the limited storage space of the user end, this is not feasible.
Jin does not disclose combining the groups of channel state information.
However, R1-141271 discloses combining the groups of channel state information.
(Section 2, pages 1-2) 2 P-CSI Reporting Aspects
Dropping the P-CSI report is not a viable option for several reasons. A first reason is that a P-CSI transmission can coincide with a PUSCH transmission and then it is not possible for the eNB to detect that the UE did not transmit P-CSI (similar to an eNB not implementing PUCCH Format 2 DTX detection). For example, as TDD DL-UL configuration 5 is a likely DL reference configuration, UEs with SPS PUSCH will be transmitting both P-CSI and SPS PUSCH in SF#2.
Even if PUCCH Format 2 DTX detection is supported by an eNB, the eNB cannot infer anything regarding whether the UE missed the DCI format IC with the configured eIMTA-RNTI from a P-CSI DTX detection as there is always the possibility of an incorrect one (the false alarm rate defined in TS 36.104 is 10%). A second reason is that PUCCH Format 2 DTX detection is an optional feature for an eNB and although a UE will not be receiving PDSCHs in DL "flexible" SFs when it operates under the fallback TDD UL-DL configuration, an eNB typically averages P-CSI reports and receiving random ones (due to absence of DTX detection when P-CSI is dropped by the UE) is clearly detrimental.
Note: “Typically averages” is being interpreted as a combination.
Jin and R1-141271 are considered to be analogous because they involve the concept of wireless communications. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jin to include the concept of combining the groups of channel state information as taught by R1-141271 so as to acquire more accurate CSI in multi-antenna technology.
Regarding Claim 35,
Jin discloses: The acquisition method according to claim 15, wherein the acquisition method further comprises: receiving information on the compression multiple corresponding to each group of second channel state information
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
determining the expansion multiple of each group of second channel state information according to the information on the compression multiple corresponding to each group of second channel state information
[0019] As a further technical solution of the invention, the connected-in-parallel multi-rate compression encoder frame PM-CsiNet + comprises eight 32 times compression encoder, are composed of fully connected layer; eight encoders connected in parallel, the decoder comprises a corresponding 4 times, 8 times, 16 times and 32 times the decompression decoder, each decoder structure and the CsiNet + decoder are the same.
Conclusion
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/JESSE P. SAMLUK/Examiner, Art Unit 2411
/DERRICK W FERRIS/Supervisory Patent Examiner, Art Unit 2411
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