OFDM水声通信系统的LS-OMP信道估计

发布时间：2018-06-09 02:05

本文选题：水声通信 + 正交频分复用　；参考：《声学技术》2017年01期

【摘要】：对于正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)水声通信系统,最小二乘(Least Squares,LS)信道估计方法受噪声影响较大,并且使用的导频数量较多,影响通信效率。而基于压缩感知理论的正交匹配追踪(Orthogonal Matching Pursuit,OMP)信道估计方法可以充分利用水声信道的稀疏特性,同时能够有效地抑制系统噪声,但控制迭代运算次数的相关参数(稀疏度或误差容忍值)是OMP算法的关键条件。针对上述问题,提出了利用少量导频随机分布的LS和OMP联合的信道估计方法,该方法首先利用少量导频采用LS方法估计出OMP算法的误差容忍值,再利用OMP算法恢复数据子载波的信道信息。理论分析和仿真结果同时表明,与传统的LS算法或OMP算法相比,新算法能够在数据恢复的同时有效抑制系统噪声,应用稀疏特性及较少量的导频,进一步提高了系统的频谱效率,对时变稀疏水声信道具有更好的适应性。
[Abstract]:For orthogonal Frequency Division Multiplexing / OFDM (orthogonal Frequency Division Multiplexing / OFDM) underwater acoustic communication system, the least square least squares (LSS) channel estimation method is greatly affected by noise, and the number of pilots used is more, which affects the communication efficiency. However, the orthogonal matching tracking orthogonal matching pursuit OMP-based channel estimation method based on compressed sensing theory can make full use of the sparse characteristics of underwater acoustic channels and effectively suppress system noise. But the key condition of OMP algorithm is to control the relative parameters (sparsity or error tolerance). In order to solve the above problems, a channel estimation method based on the combination of LS and OMP with a small number of pilots is proposed. Firstly, the error tolerance of the OMP algorithm is estimated by using the LS method with a small number of pilots. Then the OMP algorithm is used to recover the channel information of the data subcarriers. The theoretical analysis and simulation results also show that compared with the traditional LS or OMP algorithm, the new algorithm can effectively suppress the system noise while recovering the data, using sparse characteristics and a small number of pilots. The spectrum efficiency of the system is further improved and the system is more adaptable to time-varying sparse underwater acoustic channels.
【作者单位】：黑龙江科技大学理学院;黑龙江科技大学电子与信息工程学院;哈尔滨工程大学信息与通信工程学院;
【基金】：黑龙江省自然科学基金(F2015018)资助项目
【分类号】：TN929.3

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