基于虚拟时间反转镜的水声OFDM信道均衡

doi:10.11959/j.issn.1000-436x.2015011

摘要/Abstract

摘要：

针对水声信道对正交频分复用(OFDM)系统带来的符号间干扰(ISI)问题，提出了虚拟时间反转镜(VTRM)信道均衡算法，该算法具有时间压缩特性和频域相位共轭特性，可以有效缩短信道长度，减小多途信道带来的相位畸变，采用匹配追踪(MP)算法估计信道仲激响应，可以精确地估计出水声信道的幅度、时延和相位信息，为虚拟时反信道均衡提供准确的信道信息，改进了传统匹配相关信道估计方法估计精度低、无法估计信道相位信息的缺点。仿真、水池和湖上实验结果表明，OFDM水声通信系统中，VTRM信道均衡技术性能优于被动时反镜(PTRM)信道均衡和最小平方(LS)信道均衡。

关键词: 虚拟时间反转镜, 信道均衡, 匹配追踪, 正交频分复用

Abstract:

To solve the problem of the severe inter-symbol interference of orthogonal frequency division multiplexing (OFDM) communication caused by underwater acoustic multipath channel,a virtual time reversal channel equalization scheme was proposed.VTRM was able to shorten the channel impulse response and alleviate the phase distortion caused by the acoustic channel for its characteristic of the temporal compression and phase conjugation in frequency domain.The matching pursuit (MP) algorithm was applied to estimate the channel impulse response which could estimate the channel amplitude,time delay and phase accurately.It provided the channel information for the VTRM channel equalization.It also improved a lot compared to the traditional matching correlation channel estimation method which couldn't estimate the channel phase and the estimation precision was low.The simulation,pool trail and Songhua lake experiment show that the performance of VTRM channel equalization is better than that of the PTRM and LS channel equalization in the OFDM underwater acoustic communication.

Key words: virtual time reversal mirror, channel equalization, matching pursuit, orthogonal frequency division multi-plexing

尹艳玲,乔钢,刘凇佐. 基于虚拟时间反转镜的水声OFDM信道均衡[J]. 通信学报, 2015, 36(1): 90-99.

Yan-ling YIN,Gang QIAO,Song-zuo LIU. Underwater acoustic OFDM channel equalization based on virtual time reversal mirror[J]. Journal on Communications, 2015, 36(1): 90-99.

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估计信噪比/dB	估计速度/(m·s^?1)	LS解码前误码率/%	LS解码后误码率/%	PTRM解码前误码率/%	PTRM解码后误码率/%	VTRM解码前误码率/%	VTRM解码后误码率/%
21.7	0.45	10.04	6.77	8.50	5.35	6.92	2.67
20.6	0.41	8.58	4.39	7.26	0.4	6.92	0.59
21.1	0.37	10.67	13.98	7.95	4.46	8.57	3.86
21.4	0.39	10.11	9.23	8.58	2.60	7.44	1.19
23.1	0.48	7.66	2.16	7.07	1.78	5.27	0
22.5	0.35	8.50	5.88	8.02	4.31	7.77	3.57
22.7	0.37	8.42	6.28	7.07	3.83	7.71	2.22
22.1	0.29	8.65	4.53	8.72	3.79	7.47	2.08