Channel Estimation Algorithm of OFDM System Based on GSA-BP Neural Network

被引：0
|
作者：
Ji C. [1 ]
Zhang X. [1 ]
机构：
[1] School of Computer Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2022年 / 43卷 / 06期
关键词：
BP neural network; channel estimation; golden sine algorithm; least square algorithm; OFDM (orthogonal frequency division multiplexing) system;
D O I：
10.12068/j.issn.1005-3026.2022.06.002
中图分类号：
学科分类号：
摘要：
The nonlinear noise problem in orthogonal frequency division multiplexing (OFDM) system cannot be ignored. In order to understand the channel characteristics better, channel state information is needed by channel estimation. A channel estimation algorithm for OFDM system was proposed based on golden sine algorithm optimized BP neural network (GSA-BP). The problem was overcome that the traditional BP neural network algorithm is easy to fall into local extremum, and the estimation accuracy of channel estimation algorithm was improved. Firstly, the initial estimation of the channel was obtained through the LS channel estimation algorithm. Then, the accurate estimation of channel was obtained by using GSA-BP neural network. Simulation results show that the proposed algorithm has better performance than LS algorithm, and is close to MMSE algorithm, but it does not need channel prior statistics and is easy to implement. © 2022 Northeastern University. All rights reserved.
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页码：769 / 775
页数：6
相关论文
共 13 条
  • [1] Murad M, Tasadduq I A, Otero P., Linear equalization techniques for underwater acoustic OFDM communication [C], 2020 Global Conference on Wireless and Optical Technologies(GCWOT), pp. 1-5, (2020)
  • [2] Zhou En, Zhang Xing, Lyu Zhao-biao, Et al., OFDM and MIMO technology for next generation broadband wireless communication, pp. 16-17, (2008)
  • [3] Cho Y S, Kim J K, Yang W Y, Et al., c wireless communications with MATLAB [M], pp. 1-24, (2010)
  • [4] Samanta S, Sridha T V., Modified slow fading channel estimation technique and fast fading channel estimation technique for OFDM systems [C], 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1638-1643, (2018)
  • [5] Ibrahim N K, Raja Abdullah R S A, Saripan M I., Artificial neural network approach in radar target classification [J], Journal of Computer Science, 5, 1, pp. 23-32, (2009)
  • [6] Ibukahla M, Sombria J., Neural networks for modeling nonlinear memoryless communication channels [J], IEEE Transactions on Communications, 45, 7, pp. 768-771, (1997)
  • [7] Zhou X, Wang P, Yang Z, Et al., A manifold learning two-tier beam forming scheme optimizes resource management in massive MIMO networks [J], IEEE Access, 8, pp. 22976-22987, (2020)
  • [8] Ye H, Li G Y, Juang B., Power of deep learning for channel estimation and signal detection in OFDM system [J], IEEE Wireless Communications Letters, 7, 1, pp. 114-117, (2018)
  • [9] Soltani M, Pourahmadi V, Mirzaei A, Et al., Deep learning based channel estimation[J], IEEE Communications Letters, 23, 4, pp. 652-655, (2019)
  • [10] Zhang J H., The interdisciplinary research of big data and wireless channel:a cluster-nuclei based channel model [J], China Communications, 13, 2, pp. 14-26, (2016)
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