基于压缩感知的分布式无线网络研究

项目名称： 基于压缩感知的分布式无线网络研究

项目编号： No.61302084

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 徐文波

作者单位： 北京邮电大学

项目金额： 28万元

中文摘要： 经典的奈奎斯特采样定理要求采样速率至少是信号最高频率的两倍，这对于具有大量节点间交互的分布式无线网络而言，造成节点处理的数据量过高，增加了设备硬件成本和能耗。经统计分析，通信信号大都属于稀疏信号。由压缩感知理论可知，稀疏信号只需少数线性观测值即可可靠表征原始信息，从而在不影响网络性能的前提下大幅降低各节点处理的数据量。针对分布式无线网络，本项目主要研究：（1）基于压缩感知的网络传输模型及系统优化方法；（2）接收端重建方法及其理论性能；（3）衰落信道条件下的压缩感知方法。本项目旨在根据分布式无线网络特性，建立基于压缩感知的网络传输模型，提出充分利用信号特征的重建方法，解决相关优化方法的理论问题，为基于压缩感知的分布式无线网络研究提供理论基础与方案支持，为降低网络成本和能耗做出贡献。

中文关键词： 分布式无线网络；压缩感知；稀疏信号；信号重建；

英文摘要： The classic Nyquist sampling theory requires that the sampling rate is at least two times the highest frequency of the signal. For distributed wireless networks that have many interactions among nodes, this principle causes large amount of data that required to be processed at each node, which increases the device hardware costs and energy consumptions. Based on statistical analysis, most communication signals are proved to be sparse. The theory of compressed sensing states that for a sparse signal, it can be reliably represented by a small number of its linear observations. This fact significantly reduces the amount of data processed at each node without compromising the performance. For distributed wireless networks, this project mainly investigates: (1) transmission models and system optimization methods of the networks based on compressed sensing; (2) reconstruction methods at the receivers and their theoretical performance; (3) compressed sensing methods when considering fading channels. With the properties of distributed wireless networks, this project aims at creating transmission models of the networks based on compressed sensing, proposing reconstruction methods to fully exploit the signal features, solving related theoretical problems of the optimization methods, which provide theoretic basis and sche

英文关键词： distributed wireless network；compression sensing；sparse signal；signal reconstruction；