KR-20260065300-A - APPARATUS AND METHOD FOR LEARNING BASED INTERFERENCE WHITENING FOR MIMO RECEIVER

Abstract

According to one embodiment, a method of an electronic device in a wireless communication system may include: calculating a noise covariance matrix based on a received signal and an estimated channel; obtaining a sparse precision matrix from the noise covariance matrix using an iterative algorithm or a neural network; applying a compressed sparse row (CSR) to the sparse precision matrix to derive a plurality of indices for indicating non-zero elements within the matrix; and performing pre-filtering based on the plurality of indices and the estimated channel to obtain filtered channel information.

Inventors

• 서주환
• 김경연
• 최영관

Assignees

• 삼성전자주식회사

Dates

Publication Date

20260508

Application Date

20241101

Claims (20)

1. In a method of an electronic device in a wireless communication system, An operation to calculate a noise covariance matrix based on a received signal and an estimated channel; An operation to obtain a sparse precision matrix from the noise covariance matrix using an iterative algorithm or a neural network; The operation of deriving a plurality of indices for indicating non-zero elements within the matrix by applying a compressed sparse row (CSR) to the above sparse precision matrix; and A method comprising the operation of obtaining filtered channel information by performing pre-filtering based on the plurality of indices and the estimated channel.
2. In paragraph 1, the plurality of indices are, A method comprising first indices representing the values of the non-zero elements within the sparse precision matrix, second indices representing the rows of each of the first indices within the sparse precision matrix, and third indices representing the columns of each of the first indices within the sparse precision matrix.
3. In paragraph 1, A method further comprising an operation to perform SpMM (sparse matrix multiplication) operations when performing the above pre-filtering.
4. In paragraph 1, A method further comprising the operation of performing a plurality of Unfolded Layer operations using the noise covariance matrix to obtain the above sparse precision matrix.
5. In paragraph 1, An operation of storing the noise covariance matrix in response to a first command (calculate noise covariance matrix) instructing to calculate the noise covariance matrix; and A method further comprising the operation of loading the noise covariance matrix and overwriting the sparse precision matrix in response to a second command (calculate sparse precision matrix) instructing to calculate the sparse precision matrix.
6. In paragraph 5, A method further comprising the operation of loading the noise covariance matrix and storing the plurality of indices in response to a third command instructing to acquire the plurality of indices by the above CSR.
7. In paragraph 1, An operation of applying an unfolding or unrolling-based neural network to the above iterative algorithm; and A method that further includes an operation to design a trainable parameter set.
8. In electronic devices in wireless communication systems, transceiver; Memory; and It includes at least one processor, The above at least one processor enables the electronic device to perform a plurality of operations by executing instructions stored in the memory, and The above plurality of operations are, An operation to calculate a noise covariance matrix based on a received signal and an estimated channel; An operation to obtain a sparse precision matrix from the noise covariance matrix using an iterative algorithm or a neural network; The operation of deriving a plurality of indices for indicating non-zero elements within the matrix by applying a compressed sparse row (CSR) to the above sparse precision matrix; and A device comprising the operation of obtaining filtered channel information by performing pre-filtering based on the plurality of indices and the estimated channel.
9. In paragraph 8, the above plurality of indices are, An apparatus comprising first indices representing values of non-zero elements within the sparse precision matrix, second indices representing rows of each of the first indices within the sparse precision matrix, and third indices representing columns of each of the first indices within the sparse precision matrix.
10. In paragraph 8, the above plurality of operations are, A device further comprising an operation to perform SpMM (sparse matrix multiplication) operations when performing the above pre-filtering.
11. In paragraph 8, the above plurality of operations are, A device further comprising the operation of performing a plurality of Unfolded Layer operations using the noise covariance matrix to obtain the above sparse precision matrix.
12. In paragraph 8, the above plurality of operations are, An operation of storing the noise covariance matrix in response to a first command (calculate noise covariance matrix) instructing to calculate the noise covariance matrix; and A device further comprising the operation of loading the noise covariance matrix and overwriting the sparse precision matrix in response to a second command (calculate sparse precision matrix) instructing to calculate the sparse precision matrix.
13. In Clause 12, the above plurality of operations are, A device further comprising the operation of loading the noise covariance matrix and storing the plurality of indices in response to a third command instructing to acquire the plurality of indices by the above CSR.
14. In paragraph 8, the above plurality of operations are, An operation of applying an unfolding or unrolling-based neural network to the above iterative algorithm; and A device that further includes an operation for designing a trainable parameter set.
15. In a storage medium storing at least one instruction readable by a computer, The above at least one instruction causes an electronic device to perform a plurality of operations when executed by at least one processor, and The above plurality of operations are, An operation to calculate a noise covariance matrix based on a received signal and an estimated channel; An operation to obtain a sparse precision matrix from the noise covariance matrix using an iterative algorithm or a neural network; The operation of deriving a plurality of indices for indicating non-zero elements within the matrix by applying a compressed sparse row (CSR) to the above sparse precision matrix; and A storage medium comprising an operation of obtaining filtered channel information by performing pre-filtering based on the plurality of indices and the estimated channel.
16. In item 15, the above plurality of indices are, A storage medium comprising first indices representing values of non-zero elements within the sparse precision matrix, second indices representing rows of each of the first indices within the sparse precision matrix, and third indices representing columns of each of the first indices within the sparse precision matrix.
17. In paragraph 15, the above plurality of operations are, A storage medium further comprising an operation to perform SpMM (sparse matrix multiplication) operations when performing the above pre-filtering.
18. In paragraph 15, the above plurality of operations are, A storage medium further comprising the operation of performing a plurality of Unfolded Layer operations using the noise covariance matrix to obtain the above sparse precision matrix.
19. In paragraph 15, the above plurality of operations are, An operation of storing the noise covariance matrix in response to a first command (calculate noise covariance matrix) instructing to calculate the noise covariance matrix; and A storage medium further comprising the operation of loading the noise covariance matrix and overwriting the sparse precision matrix in response to a second command (calculate sparse precision matrix) instructing to calculate the sparse precision matrix.
20. In paragraph 19, the above plurality of operations are, A storage medium further comprising the operation of loading the noise covariance matrix and storing the plurality of indices in response to a third command instructing to acquire the plurality of indices by the above CSR.

Description

Apparatus and Method for Learning-Based Interference Whitening for MIMO Receiver The present disclosure relates to an apparatus and a method thereof that support a learning-based interference whitening technique in a large-scale multiple input/output receiver. In communication systems, the technology of using multiple antennas in transmitters or receivers to improve transmission quality can be referred to as multi-antenna technology. Improving transmission quality means increasing the maximum transmission speed per user, the overall capacity of a cell, or expanding cell coverage. The technology of using multiple antennas in transmitters and receivers can be referred to as Multiple Input Multiple Output (MIMO). In wireless communication systems utilizing MIMO technology, multiple antennas can be used at both the transmitter and receiver. The channel capacity of wireless communication systems utilizing MIMO technology can be significantly improved compared to single-antenna technology. Both the base station and the terminal supporting MIMO use multiple antennas, and the channel capacity can be increased in proportion to the number of antennas used. For example, if the base station uses M antennas and the terminal uses N antennas, the average transmission capacity can increase by min(M, N). The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. FIG. 1 is a block diagram of an electronic device in a network environment according to one embodiment of the present disclosure. FIG. 2 is a diagram showing an example of a DL MU-MIMO environment in a wireless communication system according to one embodiment of the present disclosure. FIG. 3 is a diagram showing an example of a DL SU-MIMO environment in a wireless communication system according to one embodiment of the present disclosure. FIG. 4 is a drawing illustrating the structure of a wireless communication system according to one embodiment of the present disclosure. FIG. 5 is a drawing illustrating the structure of a receiver according to one embodiment of the present disclosure. FIG. 6 illustrates a process for designing a set of learnable parameters for unfolding according to one embodiment of the present disclosure. FIG. 7 is a drawing for explaining an unfolding method according to one embodiment of the present disclosure. FIG. 8 is a block diagram illustrating a learning-based interference whitening method according to one embodiment of the present disclosure. FIG. 9 is a diagram illustrating a specific process of a learning-based interference whitening method according to one embodiment of the present disclosure. FIG. 10 is a diagram illustrating an unrolling-based sparse precision matrix operation according to one embodiment of the present disclosure. FIG. 11 is a diagram illustrating an Unfolded D-clime algorithm according to one embodiment of the present disclosure. FIG. 12 is a diagram illustrating Compressed Sparse Row (CSR) and Sparse Precision Matrix Multiplication (SpMM) according to one embodiment of the present disclosure. FIG. 13 shows a memory processing flowchart in an electronic device according to one embodiment of the present disclosure. Hereinafter, embodiments of the present disclosure are described in detail with reference to the drawings so that those skilled in the art can easily practice them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and brevity. The present disclosure describes embodiments using terms used in some communication standards (e.g., 3GPP (3rd Generation Partnership Project)), but this is merely illustrative. Various embodiments of the present disclosure can be easily modified and applied to other communication systems. FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to one embodiment. Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According t