1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning

ZHOU Chenhao; WEN Liyuan; QIAN Hua; KANG Kai

doi:10.11999/JEIT230239

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 886-894

ZHOU Chenhao, WEN Liyuan, QIAN Hua, KANG Kai. 1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239

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ZHOU Chenhao, WEN Liyuan, QIAN Hua, KANG Kai. 1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239

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1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning

doi: 10.11999/JEIT230239

ZHOU Chenhao^{1, 2},
WEN Liyuan^{1, 2, 3},
QIAN Hua¹,
KANG Kai^{1
,
,}

1.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China

Funds: The National Key Research and Development Program of China (2020YFB2205603),The National Natural Science Foundation of China (61971286)

Received Date: 2023-07-18
Rev Recd Date: 2023-10-23

Available Online: 2023-10-27

Publish Date: 2024-03-27

Abstract

Abstract

The base station of a massive Multiple-Input Multiple-Output (MIMO) system is equipped with hundreds of antennas, enhancing the spectral efficiency of the system and increasing the system costs. To address this problem, our research group proposed a Convergence-Guaranteed Multi-Carrier one-bit precoding (CG-MC1bit) iterative algorithm suitable for Orthogonal Frequency-Division Multiplexing (OFDM) downlink massive MIMO systems, which can ensure superior system performance. However, the corresponding computational complexity is high, hindering the practical application of the algorithm in real-time systems. To address this issue, we propose a model-driven, unfolding neural network, which is based on the CG-MC1bit iterative algorithm and introduces trainable parameters to replace high-complexity operations in forward propagation. In particular, we unfold the iterative algorithm into a neural network and introduce trainable parameters to replace high-complexity operations in forward propagation. Simulation results reveal that this method can automatically update parameters. In addition, compared with the traditional precoding algorithms, the proposed method has a higher convergence speed and lower computational complexity.
- Massive MIMO,
- Precoding,
- Orthogonal Frequency-Division Multiplexing(OFDM),
- Unfolding,
- Neural Network

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References(22)

References

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