基于壓縮感知和最小二乘的分布式協(xié)作頻譜感知
發(fā)布時間:2018-09-01 14:53
【摘要】:針對認(rèn)知無線電(CR)集中式頻譜感知算法對融合中心要求高,而且對節(jié)點(diǎn)失效的容忍性也不高等缺點(diǎn),提出了一種基于壓縮感知的分布式多節(jié)點(diǎn)協(xié)作算法。認(rèn)知無線電網(wǎng)絡(luò)中每個CR節(jié)點(diǎn)在接收信號頻譜后,首先根據(jù)壓縮采樣理論在本地獲取壓縮采樣測量值,然后利用l_1范數(shù)約束的最小二乘算法在本節(jié)點(diǎn)估計頻譜,把在此節(jié)點(diǎn)估計的頻譜傳給下一相鄰節(jié)點(diǎn),以此進(jìn)行迭代優(yōu)化直到算法收斂。理論分析和仿真結(jié)果表明,所提算法不僅計算復(fù)雜度低,收斂速度快,而且精確重構(gòu)性能好,可靠性較高。
[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.
【作者單位】: 北京信息科技大學(xué)信息與通信工程學(xué)院;
【基金】:國家自然科學(xué)基金資助項目(61302073) 北京市自然科學(xué)基金資助項目(4172021,Z160002) 北京市教育委員會科技發(fā)展計劃面上項目(KM201711232010)
【分類號】: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.
【作者單位】: 北京信息科技大學(xué)信息與通信工程學(xué)院;
【基金】:國家自然科學(xué)基金資助項目(61302073) 北京市自然科學(xué)基金資助項目(4172021,Z160002) 北京市教育委員會科技發(fā)展計劃面上項目(KM201711232010)
【分類號】:TN925
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