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US-20260128962-A1 - COMMUNICATION METHOD AND COMMUNICATION APPARATUS

US20260128962A1US 20260128962 A1US20260128962 A1US 20260128962A1US-20260128962-A1

Abstract

Embodiments of the present application provide a communication method and a communication apparatus. The communication method includes: obtaining Q group(s) of first data sample(s) corresponding to Q layer(s) of an AI model, where the Q group(s) of the first data sample(s) is from compressed Q group(s) of first raw data sample(s) which is compressed according to Q transformation matrix(es), the Q group(s) of the first data sample(s) is related to an inference cycle of the AI model, and Q is a positive integer; and sending the Q group(s) of the first data sample(s).

Inventors

  • Yiqun Ge
  • Hao Tang
  • Jianglei Ma

Assignees

  • HUAWEI TECHNOLOGIES CO., LTD.

Dates

Publication Date
20260507
Application Date
20251211

Claims (20)

  1. 1 . A communication method, comprising: obtaining Q group(s) of first data sample(s) corresponding to Q layer(s) of an artificial intelligence (AI) model, wherein the Q group(s) of the first data sample(s) is from compressed Q group(s) of first raw data sample(s) compressed according to Q transformation matrix(es), the Q group(s) of the first data sample(s) is related to an inference cycle of the AI model, and Q is a positive integer; and sending the Q group(s) of the first data sample(s).
  2. 2 . The communication method according to claim 1 , further comprising: sending first information indicating the Q transformation matrix(es).
  3. 3 . The communication method according to claim 2 , wherein the first information further indicates Q sampling matrix(es), the Q sampling matrix(es) is used to sample Q group(s) of second raw data sample(s), and the Q transformation matrix(es) is used to compress sampling result(s) of the Q group(s) of the second raw data sample(s) into Q group(s) of second data sample(s).
  4. 4 . The communication method according to claim 1 , further comprising: receiving second information indicating difference(s) between q group(s) of second data sample(s) and q group(s) of the first data sample(s) in the Q group(s) of the first data sample(s), wherein the q group(s) of the second data sample(s) is based on inputs or outputs of q layer(s) in the Q layer(s) during the inference cycle, q is a positive integer, and q≤Q.
  5. 5 . The communication method according to claim 4 , wherein the difference(s) between the q group(s) of the second data sample(s) and the q group(s) of the first data sample(s) is used to check whether the inference cycle is abnormal.
  6. 6 . The communication method according to claim 1 , further comprising: sending third information indicating correspondence between the Q layer(s) and the Q group(s) of the first data sample(s).
  7. 7 . An apparatus, comprising: at least one processor coupled with a memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform operations, wherein the operations comprise: obtaining Q group(s) of first data sample(s) corresponding to Q layer(s) of an artificial intelligence (AI) model, wherein the Q group(s) of the first data sample(s) is from compressed Q group(s) of first raw data sample(s) compressed according to Q transformation matrix(es), the Q group(s) of the first data sample(s) is related to an inference cycle of the AI model, and Q is a positive integer; and sending the Q group(s) of the first data sample(s).
  8. 8 . The apparatus according to claim 7 , the operations further comprising: sending first information indicating the Q transformation matrix(es).
  9. 9 . The apparatus according to claim 8 , wherein the first information further indicates Q sampling matrix(es), the Q sampling matrix(es) is used to sample Q group(s) of second raw data sample(s), and the Q transformation matrix(es) is used to compress sampling result(s) of the Q group(s) of the second raw data sample(s) into Q group(s) of second data sample(s).
  10. 10 . The apparatus according to claim 7 , the operations further comprising: receiving second information indicating difference(s) between q group(s) of second data sample(s) and q group(s) of the first data sample(s) in the Q group(s) of the first data sample(s), wherein the q group(s) of the second data sample(s) is based on inputs or outputs of q layer(s) in the Q layer(s) during the inference cycle, q is a positive integer, and q≤Q.
  11. 11 . The apparatus according to claim 10 , wherein the difference(s) between the q group(s) of the second data sample(s) and the q group(s) of the first data sample(s) is used to check whether the inference cycle is abnormal.
  12. 12 . The apparatus according to claim 7 , the operations further comprising: sending third information indicating correspondence between the Q layer(s) and the Q group(s) of the first data sample(s).
  13. 13 . The apparatus according to claim 7 , the operations further comprising: sending fourth information indicating Q scoring function(s), wherein the Q scoring function(s) is used to measure difference(s) between the Q group(s) of the first data sample(s) and Q group(s) of second data sample(s), and the Q group(s) of the second data sample(s) is based on inputs or outputs of the Q layer(s).
  14. 14 . An apparatus, comprising: at least one processor coupled with a memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform operations, wherein the operations comprise: receiving Q group(s) of first data sample(s) corresponding to Q layer(s) of an artificial intelligence (AI) model, wherein the Q group(s) of the first data sample(s) is from compressed Q group(s) of first raw data sample(s) compressed according to Q transformation matrix(es), the Q group(s) of the first data sample(s) is related to an inference cycle of the AI model, and Q is a positive integer.
  15. 15 . The apparatus according to claim 14 , the operations further comprising: receiving first information indicating the Q transformation matrix(es).
  16. 16 . The apparatus according to claim 15 , wherein the first information further indicates Q sampling matrix(es), the Q sampling matrix(es) is used to sample Q group(s) of second raw data sample(s), and the Q transformation matrix(es) is used to compress sampling result(s) of the Q group(s) of the second raw data sample(s) into Q group(s) of second data sample(s).
  17. 17 . The apparatus according to claim 14 , the operations further comprising: sending second information indicating difference(s) between q group(s) of second data sample(s) and q group(s) of the first data sample(s) in the Q group(s) of the first data sample(s), wherein the q group(s) of the second data sample(s) is based on inputs or outputs of q layer(s) in the Q layer(s) during the inference cycle, q is a positive integer, and q≤Q.
  18. 18 . The apparatus according to claim 17 , wherein the difference(s) between the q group(s) of the second data sample(s) and the q group(s) of the first data sample(s) is used to determine whether the inference cycle of the AI model is abnormal.
  19. 19 . The apparatus according to claim 14 , the operations further comprising: receiving third information indicating correspondence between the Q layer(s) and the Q group(s) of the first data sample(s).
  20. 20 . The apparatus according to claim 14 , the operations further comprising: receiving fourth information indicating Q scoring function(s), wherein the Q scoring function(s) is used to measure difference(s) between the Q group(s) of the first data sample(s) and Q group(s) of second data sample(s), and the Q group(s) of the second data sample(s) is based on inputs or outputs of the Q layer(s).

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application is a continuation of International Application No. PCT/CN2023/125044, filed on Oct. 17, 2023, which claims priority to U.S. Provisional Patent Application No. 63/507,872, filed on Jun. 13, 2023. The disclosures of the aforementioned applications are hereby incorporated by reference in their entirety. TECHNICAL FIELD Embodiments of the present application relate to the field of communications, and more specifically, to a communication method and a communication apparatus. BACKGROUND Artificial intelligence (AI)-based algorithms have been introduced into wireless communications to solve some wireless problems such as channel estimation, scheduling, channel state information (CSI) compression, positioning, beam-management, and so on. AI algorithm is a data-driven method that tunes some pre-defined architectures by a set of data samples called as training data set. During the inference cycle of the AI model, data needs to be transmitted. Raw data may include user privacy. It may be against the privacy policy to transmit raw data. In addition, transmitting raw data may consume a lot of resources and is inefficient. Therefore, an urgent technical problem that needs to be solved is how to improve data transmission efficiency. SUMMARY Embodiments of the present application provide a communication method and a communication apparatus. The technical solutions may improve data transmission efficiency. According to a first aspect, an embodiment of the present application provides a communication method, including obtaining Q group(s) of first data sample(s) corresponding to Q layer(s) of an AI model, where the Q group(s) of the first data sample(s) is from compressed Q group(s) of first raw data sample(s) which is compressed according to Q transformation matrix(es), the Q group(s) of the first data sample(s) is related to an inference cycle of the AI model, and Q is a positive integer; and sending the Q group(s) of the first data sample(s). According to the above technical solution, the first data sample is a low-dimensional data sample which is compressed according to a transformation matrix. In this way, the bandwidth for the first data sample(s) can be saved and data transmission efficiency can be improved. At the same time, first raw data can be protected. Each group may correspond to one layer of the AI model. Different groups may correspond to different layers. In a possible design, the method further includes: sending first information indicating the Q transformation matrix(es). Optionally, a transformation matrix be a unitary matrix or an orthonormal matrix. Optionally, each basis vector of a transformation matrix may be a standard basis such as Fourier basis, DCT basis, wavelet basis, or the like. In a possible design, the first information is further configured to indicate Q sampling matrix(es), the Q sampling matrix(es) is configured to sample Q group(s) of second raw data sample(s), and the Q transformation matrix(es) is configured to compress sampling result(s) of the Q group(s) of the second raw data sample(s) into Q group(s) of second data sample(s). Optionally, a sampling matrix may be a random matrix or a pseudo-random matrix. According to the above technical solution, the data sample can be obtained by compressing the raw data sample according to the sampling matrix and the transformation matrix. The dimensions of the sampling matrix and transformation matrix are smaller, which is beneficial to reducing the resources required for transmitting the sampling matrix and transformation matrix, thereby improving transmission efficiency. In a possible design, the method further includes: receiving second information indicating difference(s) between q group(s) of second data sample(s) and q group(s) of the first data sample(s) in the Q group(s) of the first data sample(s), where the q group(s) of the second data sample(s) is based on inputs or outputs of q layer(s) in the Q layer(s) during the inference cycle, and q is a positive integer, q≤Q. For a first data sample and a second data sample corresponding to the same layer, the distance between the first data sample and the second data sample is approximately the same as the distance between the first raw data sample and the second raw data sample. In this way, computational complexity can be reduced, which is beneficial to improving processing efficiency. In a possible design, the difference(s) between the q group(s) of the second data sample(s) and the q group(s) of the first data sample(s) is configured to check whether the inference cycle is abnormal. For example, if the distances corresponding to all the groups are consistently below the corresponding threshold(s), the current inference cycle may be considered normal. According to the above technical solution, the difference(s) can be used to check whether the current inference cycle works as expected, which is conducive to ensuring the comm