基于神经网络的高并行大规模MIMO信号检测算法

doi:10.12305/j.issn.1001-506X.2022.12.30

Abstract

Abstract:

Massive multiple input multiple output (MIMO) is one of the key technologies, as 5G and future mobile wireless networks continue to evolve. With the increasing number of antennas, the design of receivers has brought a huge challenge, and detection algorithms with too much complexity are difficult to be applied in practice. In this paper, a high-parallelism (HP) detection algorithm is developed into a neural network, the single-layer neural network is based on each iteration of this algorithm, which is combined with trainable weighted parameters and nonlinear neural units, and the network structure HP-Net is proposed. Optimal trainable parameters are obtained by training the HP-Net, which in turn improves the detection performance. The experimental results show that the paper method is less complex and can obtain lower bit error rate (BER) than the traditional minimum mean square error (MMSE) algorithm, and has better BER performance than the HP detection algorithm.

Key words: massive multiple input multiple output (MIMO), deep neural network (DNN), signal detection

CLC Number:

TN929.53

Yaohua XU, Chenglong ZHU, Yi WANG, Fang JANG, Mengqin DING, Huiping WANG. Neural network-based algorithm for high-parallelism massive MIMO signal detection[J]. Systems Engineering and Electronics, 2022, 44(12): 3843-3849.

Figures/Tables 12

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References 17

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迭代次数		算法
迭代次数		HP	HP-Net	NSA
乘法	iter=3	2K²+5K	5K²+7K	K³
	iter=4	3K²+5K	7K²+7K	2K³－K²
	iter= n(n > 3)	(n－1)·K²+5K	(2n-1)·K²+7K	(n－2)·K³－(n－3)·K²
除法		K/2	K/2	K
并行性		K	K	K

HP	HP-Net	MMSE	NSA
O(K²)	O(K²)	O(K³)	O(K³)

算法	层数	参数个数
HP-Net	L	2L
DET-Net	L	6L

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Neural network-based algorithm for high-parallelism massive MIMO signal detection

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