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基于改进的SOMP水声通信信道联合估计方法
Research on the improved joint estimation method of SOMP for underwater acoustic communication channel
葛慧林, 梁仕杰
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作者单位:江苏科技大学, 江苏 镇江 212002
中文关键字:信道估计;压缩感知;联合稀疏模型;正交频分复用;同步正交匹配追踪算法
英文关键字:the channel estimation; compressed sensing; joint sparse model; orthogonal frequency division multiplexing(OFDM); simultaneous orthogonal matching pursuit algorithm
中文摘要:水声通信信道通常表现出时变特征。然而在某些通信条件下,水声信道的变化速率相对发送的OFDM符号周期而言仍然较慢,整体呈现缓慢时变特性,而且研究表明在相邻时隙内的信道结构具有较强的时间相关性。如何利用好信道的缓变特征,设计适合的OFDM信道估计方法,对进一步改善水声通信信道估计性能具有重要意义。分布式压缩感知(Distributed Compressed Sensing,DCS)理论通过利用多个信号的共同稀疏性进行联合重构,能够进一步提高稀疏重建性能。因此本文将以此为基础,在DCS理论框架下,通过对同步正交匹配追踪(Simultaneous Orthogonal Matching Pursuit,SOMP)算法进行改进,构造一种时域联合的信道估计方法并通过仿真与其他类似算法进行性能比较。
英文摘要:Underwater acoustic communication channel usually show the feature of time-varying. However, under certain communication conditions, the change of the underwater acoustic channel rate is still slower than the sending OFDM symbol cycle. The whole presents a slow time-varying characteristic and the channel structure within the adjacent time slot has strong time correlation. How to use the slow - changing characteristics of the channel to design the suitable OFDM channel estimation method is of great significance to further improve the underwater acoustic communication channel estimation performance. Distributed Compressed Sensing (DCS) theory can further improve the performance of sparse reconstruction by joint reconstruction using the common sparsity of multiple signals. On this basis, this article will build a time domain channel estimation method with joint structure in the DCS theory framework by improving the synchronous Orthogonal Matching Pursuit (Simultaneous Orthogonal Matching Pursuit, SOMP) algorithm and be compared with other similar algorithms on performance through the simulation.
2020,42(2): 140-143 收稿日期:2019-01-03
DOI:10.3404/j.issn.1672-7649.2020.02.027
分类号:TN929.3
基金项目:江苏省自然科学基金资助项目(BK20161359)
作者简介:葛慧林(1989-),男,助理研究员、研究方向为通信与网络,智能控制
参考文献:
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