一种新型的基于深度学习的时变信道预测方法

doi:10.11959/j.issn.1000-0801.2021011

Abstract

Abstract:

For high-speed mobile orthogonal frequency division multiplexing (OFDM) systems, a novel time-varying channel prediction method based on deep learning was proposed.To avoid the influence caused by random initialization of network parameters, the proposed method firstly obtains an ideal channel estimation based on data and pilot, and then pre-trains the back propagation (BP) neural network based on the channel estimation to obtain the ideal network initial parameters.Then, based on the initial network value obtained by pre-training, the proposed method uses the channel estimation based on pilot to train the BP neural network again.Finally, the proposed method realizes the single-time-and multi-time prediction of time-varying channels through on-line prediction.Simulation results show that the proposed method can significantly improve the prediction accuracy of time-varying channels and has a low computational complexity.

Key words: high-speed mobility, OFDM, deep learning, BP neural network, time-varying channel prediction, pre-training

CLC Number:

TN911

Jie ZHANG, Lihua YANG, Zenghao WANG, Bo HU, Qian NIE. A novel deep learning based time-varying channel prediction method[J]. Telecommunications Science, 2021, 37(1): 39-47.

Figures/Tables 7

信道预测方法	复杂度
参考文献[6]方法	O(N_AR )
参考文献[12]方法	$O (\max (M, N_{nz}, N))$
参考文献[13]方法	线上	O(max(D×Q,Q×P))
	线下	$O (\max (\tilde{N} (D \times Q), \tilde{N} (Q \times P \times Q)))$
本文方法	线上	Omax (D×Q,Q×P)
	线下	预训练	$O (\max (\tilde{N} (D \times Q), \tilde{N} (Q \times P \times Q)))$
		训练	$O (\max (\tilde{N} (D \times Q), \tilde{N} (Q \times P \times Q)))$

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训练样本数/个	参考文献[13]	本文方法
50	1.127 957	0.417 858
250	4.171 226	2.643 204
750	5.050 872	4.760 230
1 000	15.350 929	9.153 231
5 000	75.518 988	41.488 527
25 000	141.240 442	82.513 299

A novel deep learning based time-varying channel prediction method

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