Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network

SHEN Bin; YANG Jian; ZENG Xiangzhi; CUI Taiping

doi:10.11999/JEIT211276

Volume 45 Issue 1

Jan. 2023

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SHEN Bin, YANG Jian, ZENG Xiangzhi, CUI Taiping. Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276

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SHEN Bin, YANG Jian, ZENG Xiangzhi, CUI Taiping. Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276

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Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network

doi: 10.11999/JEIT211276

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078)

Received Date: 2021-11-16
Rev Recd Date: 2022-03-28

Available Online: 2022-04-18

Publish Date: 2023-01-17

Abstract

Abstract

In recent years, deep learning has become one of the key technologies in the field of wireless communication. In a series of MIMO signal detection algorithms based on deep learning, most of them do not fully consider the interference cancellation problem between adjacent antennas, hence the impact of multi-user interference on the bit error rate performance can not be completely eliminated. To this end, a method that combines deep learning and Successive Interference Cancellation (SIC) algorithms for uplink signal detection in a massive MIMO system is propesed. Firstly, by optimizing the traditional Detection Network (DetNet) and improving the ScNet (Sparsely connected neural Network), a detection algorithm based on the Deep Neural Network (DNN), called Improved ScNet (ImpScNet), is proposed. On this basis, the SIC is applied to the design of the deep learning framework structure, and a massive MIMO multi-user SIC detection algorithm based on deep learning is proposed, which is called ImpScNet-SIC. This algorithm is divided into two stages on each detection layer. The first stage is provided by the ImpScNet algorithm proposed in this paper to provide the initial solution, and then the initial solution is demodulated to the corresponding constellation point as the input of the SIC, which constitutes the second stage. In addition, the ImpScNet algorithm is also used in SIC to estimate the transmitted symbols in order to obtain the best performance. Simulation results show that, compared with various typical representative algorithms, the ImpScNet-SIC detection algorithm proposed in this paper is particularly suitable for the massive MIMO signal detection. It has the advantages of fast convergence speed, stable convergence and relatively low complexity. And there is at least 0.5 dB gain in 10^–3 bit error rate.
- Signal detection,
- Deep learning,
- Multi-user interference,
- Massive MIMO,
- Sparse connection,
- Successive Interference Cancellation (SIC)

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References(25)

References

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