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Semi-blind channel estimation of MIMO-OFDM systems based on improved PSO-hopfield neural network
Jie, Hao1,2
2018-03-24
会议日期March 24, 2018 - March 25, 2018
会议地点Phuket, Thailand
关键词Blind equalization Cost functions Gain control Heuristic algorithms Hopfield neural networks Intelligent systems Iterative methods MIMO systems Orthogonal frequency division multiplexing Particle swarm optimization (PSO) Swarm intelligence Channel response Estimation methods Hopfield Networks Improved PSO Improved pso algorithms MIMO-OFDM systems Semi-blind Semi-blind channel estimation
DOI10.1145/3206185.3219779
页码153-157
英文摘要Channel estimation is the key part of the design of any receiver, this paper proposed a semi-blind channel estimation method of MIMO-OFDM systems based on improved Hopfield neural network. First, the proposed method improved the semi-blind cost function for neural network based on LS criterion and LMMSE criterion. Then, this paper proposed a new structure of Hopfield network to substitute the function iteration, and the improved PSO algorithm was also used to optimize the parameter of Hopfield network. Finally, The new estimation method was proposed to estimate the channel response. The comparison of channel estimation by the proposed method and RBF network were present, the results validated that the proposed method is feasible to estimate the channel response, and that the precision of estimation is improved, which will be useful for the future channel estimation of MIMO-OFDM systems. © 2018 Association for Computing Machinery.
会议录ACM International Conference Proceeding Series
会议录出版者Association for Computing Machinery
语种英语
内容类型会议论文
源URL[http://ir.lut.edu.cn/handle/2XXMBERH/118085]  
专题兰州理工大学
作者单位1.Lanzhou University of Technology, School of Computer and Communication, Lanzhou, China
2.Northwest Minzu University, School of Electrical Engineering, Lanzhou, China;
推荐引用方式
GB/T 7714
Jie, Hao. Semi-blind channel estimation of MIMO-OFDM systems based on improved PSO-hopfield neural network[C]. 见:. Phuket, Thailand. March 24, 2018 - March 25, 2018.
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