认知无线电中宽带频谱感知研究

认知无线电中宽带频谱感知研究(论文18000字)
摘　要
由于无线通信的快速增长，目前现有的固定频谱分配方式使得可用频谱资源越来越紧缺，且大部分已授权频段利用率极低，造成了频谱资源的极大浪费。认知无线电在软件无线电的基础上发展而来，可通过对外界频谱环境的持续感知，及时发现并利用授权频段内尚未被主用户利用的空闲频谱，是一种可以有效提高频谱资源利用效率的智能无线电技术，也能够有效缓解日益严峻的频谱资源稀缺问题。频谱感知技术是构建认知无线电系统、实现认知无线电应用的核心技术，也是保证认知无线电系统发现并充分利用频谱资源、保护授权主用户免受有害干扰的重要前提。由于无线通信业务的高速发展，人们对无线频谱资源需求急剧增加，为了进一步提高对频谱资源的利用，宽带频谱感知技术成为认知无线电领域的重要研究方向。要实现宽频带上快速准确的频谱感知需对宽带信号进行高速采样，而越来越高的采样速度不仅难以实现，而且还会带来极大的采样开销和硬件复杂度，使其逐渐成为宽带频谱感知技术的瓶颈。近年来在认知无线电宽带谱感知中得到高度关注的压缩感知理论(Compressed Sensing, CS)，其非相关测量过程能够实现宽带信号的低速无损采样，有望解决宽带频谱感知中采样率过高的问题，为实现低采样开销下的快速宽带频谱感知提供了一种可行途径。
压缩感知利用宽带无线信号的频域稀疏特性，能够在低于Nyquist速率的采样下利用少量观测数据来实现宽带频谱估计和空穴检测。但相关频谱压缩感知算法的性能并不理想，为了实现宽带信道的快速准确感知，本文基于宽带信道的时频统计特性，在传统基追踪去噪算法(BPDN)的基础上提出了一种优化的加权基追踪去噪算法(WBPDN)。该算法利用子频段历史平均功率密度水平来构建各子频段权重以优化目标函数，改善算法性能。实验结果表明：该算法能通过少量观测数据准确重构宽带信道的谱估计，且比传统BPDN算法具有更好的压缩性能和更小的重构误差；另外加权后的算法收敛速度更快，显著减少了算法所需的运行时间。
关键词：认知无线电；宽带频谱感知；压缩感知；加权基追踪；动态频谱接入
Research on Wideband Spectrum Sensing of Cognitive Radio
ABSTRACT
With the rapid growth of wireless communication, the conventional fixed spectrum allocation rules have resulted in both scarcity of available spectrum resources and low spectrum usage efficiency in almost all currently deployed frequency bands. Based on the developing of software-defined radio, cognitive radio is an intelligent radio technology. It is able to discover and utilize idle authorized spectrum which is allocated to primary user but not being used temporarily by sensing external wireless spectrum environment persistently. Cognitive radio can efficiently solve the growing problem of spectrum resource scarcity. As the core technology of constructing and implementing cognitive radio, spectrum sensing enables cognitive radio system to discover and utilize spectrum resource, while protecting authorized primary user from harmful interference. Due to the rapid increase of wireless services and growing need of wireless spectrum resource, wide-band spectrum sensing has become an important research direction in the field of cognitive radio to improve the utilization of spectrum resource. To achieve fast and accurate spectrum sensing over a wide bandwidth, a corresponding large sampling rate is required, which is very challenging for practical implementation. Fortunately, a large part of the frequency range is vacant, that is, the signal is frequency-domain sparse. We can use the recently developed compressive sensing to reduce the sampling rate by a large margin. It provides an alternative approach for fast and accurate wideband spectrum sensing with low cost of sampling.
Compressive sensing (CS) can achieve spectral detection and estimation from far fewer samples at sub-Nyquist sampling rates. However, efforts to design CS reconstruction algorithms for wideband spectrum sensing are very limited. Considering the characteristics of spectrum distribution are partially known in advance, we proposed a new algorithm called weighted basis pursuit denoising (WBPDN), which is based on the famous  -minimization algorithm to add prior information on the support of the sparse domain. The WBPDN incorporates the statistical properties of spectrum usages to reweight the coefficients of the previous  -minimization algorithm. This underlying optimization corresponds to encouraging nonzero coefficients to gain performance improvement. There are empirical evidences of the fact that the proposed algorithm not only guarantees accurate spectral estimation from fewer measurements, but also outperforms basis pursuit denoising (BPDN) in terms of measurements requirements and reconstruction error. Moreover, WBPDN has faster convergence speed and significantly reduces the computation time required by BPDN.
Key words: cognitive radio; wideband spectrum sensing; compressive sensing; weighted basis pursuit denoising; dynamic spectrum access

目  录
摘  要    I
ABSTRACT    II
第一章  绪论    1
1.1研究背景及意义    1
1.2认知无线电概述    2
1.3 宽带频谱感知技术    5
1.4 主要研究内容和章节安排    7
第二章  压缩感知理论    9
2.1引言    9
2.2压缩感知理论介绍    9
2.3 压缩感知重构算法    12
2.4 本章小结    13
第三章 基于加权基追踪算法的宽带压缩频谱感知    14
3.1 引言    14
3.2 宽带压缩频谱感知模型    14
3.3 加权谱重构算法    18
3.4仿真结果与算法性能分析    21
3.5 本章小结    24
第四章 结语    25
参考文献    26
致  谢    30
附 录    31