Compressive channel estimation techniques for narrowband or wideband communications systems

用于窄带或宽带通信系统的压缩信道估计技术

• 批准号: 26889050
• 负责人: 桂 冠
• 金额: $ 1.75万
• 依托单位: Akita Prefectural University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Research Activity Start-up
• 财政年份: 2014
• 资助国家: 日本
• 起止时间: 2014-08-29 至 2016-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-26889050/
• 关键词: compressive sensing; adaptive filter; channel estimation; sparse constraint; 先端的通信; 圧縮センシング; 情報通信工学; 圧縮チャネル推定; スパース適応チャネル推定; スパース適応フィルタ; 非ガウス雑音

Adaptive sparse channel estimation techniques have been studied in different wireless communication systems. To reduce computational complexity and to achieve the robustness in different noise environment, in this project, our research results are summarized as follows:1) Based on the D-scale channel model, we proposed two algorithms: sparse normalized least mean fourth (NLMF) algorithm and mixed least square/fourth (LMS/F) algorithm. The two proposed algorithms can take advantage of the channel sparsity in the D-scale channel. Computer simulation results have been provided to confirm the effectiveness of the proposed algorithms.2) Based on the non-Gaussian noise model, we proposed a kinds of stable sparse sign least mean square (SLMS) algorithms to mitigate impulsive noise and to exploit channel sparsity. Representative simulations have been given to validate the proposed algorithms. In addition, regularization parameter selection for the proposed algorithm has been investigated in this project. The selected parameter can improve the estimation performance while accelerate the convergence speed for the proposed algorithms.

自适应稀疏信道估计技术已经在不同的无线通信系统中被研究。为了降低计算复杂度，并在不同的噪声环境下获得较好的鲁棒性，本课题的研究工作主要包括以下几个方面：1）基于D尺度信道模型，提出了两种算法：稀疏归一化最小均方四阶（NLMF）算法和混合最小平方/四阶（LMS/F）算法。这两种算法可以充分利用D尺度信道的稀疏性。计算机仿真结果验证了算法的有效性。2）基于非高斯噪声模型，提出了一种稳定的稀疏符号最小均方（SLMS）算法，以抑制脉冲噪声并充分利用信道稀疏性。 有代表性的仿真已经给出了验证所提出的算法。此外，正则化参数的选择所提出的算法已在本项目中进行了研究。该参数的选取在提高估计性能的同时加快了算法的收敛速度。

项目成果

Block Bayesian sparse learning algorithms with application to estimating channels in OFDM systems

• DOI: 10.1109/wpmc.2014.7014823
• 发表时间: 2014-07
• 期刊: 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC)
• 作者: Guan Gui;Li Xu;Lin Shan

Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments

• DOI: 10.1109/hmwc.2014.7000208
• 发表时间: 2014-11
• 期刊: 2014 International Workshop on High Mobility Wireless Communications
• 作者: Guan Gui;Li Xu;F. Adachi

RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

• DOI: 10.1186/1687-6180-2014-125
• 发表时间: 2014-03
• 期刊: EURASIP Journal on Advances in Signal Processing
• 影响因子: 1.9
• 作者: Guan Gui;Li Xu;F. Adachi

A matching pursuit generalized approximate message passing algorithm

一种匹配追踪广义近似消息传递算法

• 发表时间: 2015
• 期刊: IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
• 作者: Yongjie Luo;Qun Wan;Guan Gui;Fumiyuki Adachi
• 通讯作者: Fumiyuki Adachi

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