稀疏无线信道决策反馈均衡的压缩感知技术

Evangelos Vlachos, A. Lalos, Giannis Lionas, K. Berberidis
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引用次数: 2

摘要

本文针对具有长且稀疏脉冲响应的信道,提出了一种高效的决策反馈均衡(DFE)方法。在对信道脉冲响应(CIR)系数的合理假设下,前馈(FF)和反馈(FB)滤波器也可以用稀疏滤波器逼近。可以利用CIR的稀疏性或DFE滤波器的稀疏性来推导DFE的有效实现。为此,压缩采样(CS)方法已经在系统识别设置中取得了成功,可以显著提高非稀疏感知DFE的性能。在基追踪和匹配追踪技术的基础上,提出了新的DFE方案,具有显著的计算节省、性能提高和训练序列要求短的特点。为了研究所提方案的性能,还研究了常见DFE设置下的受限等距特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compressed Sensing Techniques for Decision Feedback Equalization of Sparse Wireless Channels
In this paper new efficient decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses are proposed. It has been shown that under reasonable assumptions concerning the channel impulse response (CIR) coefficients, the feedforward (FF) and feedback (FB) filters may be also approximated by sparse filters. Either the sparsity of the CIR, or the sparsity of the DFE filters may be exploited to derive efficient implementations of the DFE. To this end, compressed sampling (CS) approaches, already successful in system identification settings, can significantly improve the performance of the non sparsity aware DFE. Building on basis pursuit and matching pursuit techniques new DFE schemes are proposed that exhibit considerable computational savings, increased performance properties and short training sequence requirements. To investigate the performance of the proposed schemes the restricted isometry property in the common DFE setup is also investigated.
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