基于压缩感知和最小二乘的分布式协作频谱感知
发布时间：2018-09-01 14:53
【摘要】：针对认知无线电(CR)集中式频谱感知算法对融合中心要求高,而且对节点失效的容忍性也不高等缺点,提出了一种基于压缩感知的分布式多节点协作算法。认知无线电网络中每个CR节点在接收信号频谱后,首先根据压缩采样理论在本地获取压缩采样测量值,然后利用l_1范数约束的最小二乘算法在本节点估计频谱,把在此节点估计的频谱传给下一相邻节点,以此进行迭代优化直到算法收敛。理论分析和仿真结果表明,所提算法不仅计算复杂度低,收敛速度快,而且精确重构性能好,可靠性较高。
[Abstract]:A distributed multi-node cooperative algorithm based on compressed sensing is proposed to overcome the shortcomings of centralized spectrum sensing algorithm based on cognitive radio (CR) which requires high fusion center and low tolerance for node failure. After receiving the spectrum of the signal, each CR node in the cognitive radio network first acquires the compressed sampling measurements locally according to the compression sampling theory, and then estimates the spectrum at this node by using the L1-norm constrained least squares algorithm. The estimated spectrum at this node is transmitted to the next adjacent node, and then the iterative optimization is carried out until the algorithm converges. Theoretical analysis and simulation results show that the proposed algorithm not only has the advantages of low computational complexity, fast convergence rate, but also good precision reconstruction performance and high reliability.
【作者单位】： 北京信息科技大学信息与通信工程学院;
【基金】：国家自然科学基金资助项目(61302073) 北京市自然科学基金资助项目(4172021,Z160002) 北京市教育委员会科技发展计划面上项目(KM201711232010)
【分类号】：TN925
[Abstract]:A distributed multi-node cooperative algorithm based on compressed sensing is proposed to overcome the shortcomings of centralized spectrum sensing algorithm based on cognitive radio (CR) which requires high fusion center and low tolerance for node failure. After receiving the spectrum of the signal, each CR node in the cognitive radio network first acquires the compressed sampling measurements locally according to the compression sampling theory, and then estimates the spectrum at this node by using the L1-norm constrained least squares algorithm. The estimated spectrum at this node is transmitted to the next adjacent node, and then the iterative optimization is carried out until the algorithm converges. Theoretical analysis and simulation results show that the proposed algorithm not only has the advantages of low computational complexity, fast convergence rate, but also good precision reconstruction performance and high reliability.
【作者单位】： 北京信息科技大学信息与通信工程学院;
【基金】：国家自然科学基金资助项目(61302073) 北京市自然科学基金资助项目(4172021,Z160002) 北京市教育委员会科技发展计划面上项目(KM201711232010)
【分类号】：TN925
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