基于压缩感知信道能量观测的协作频谱感知算法

压缩感知为认知无线电宽带频谱感知提供了一种新思路。基于压缩感知原理,该文提出一种不需要重构宽带频谱本身,而是直接重构各信道能量的协作频谱感知方法。多个次用户使用宽带随机滤波器组获取信道能量的观测值。融合中心同步接收多个用户的能量观测,并利用同步稀疏自适应匹配追踪协作重构算法重构所有次用户的信道能量。仿真结果表明加性高斯白噪声环境下该协作感知方法所需的滤波器数目仅为传统方法的20%左右,瑞利衰落信道下也仅需传统方法的40%,有效降低了系统复杂度并改善感知性能。同时,该文提出的同步稀疏自适应匹配追踪算法对比经典的同步正交匹配追踪算法在重构精度及算法复杂度两方面都有所提升。     

利用组稀疏特性的宽带压缩频谱感知

基于认知无线电的动态频谱接入需对宽带信道进行频谱感知,而越来越高的采样速率日益成为宽带频谱感知的瓶颈。压缩感知作为一种新的信号获取技术为亚奈奎斯特采样速率下的宽带频谱感知提供了一种可行方案。在相关应用场景中,如果能够挖掘相关先验信息并在重构算法中整合这些信息,将大幅提高压缩感知的性能。本文基于压缩感知技术,利用信道的划分信息及宽带信号的组稀疏特性,提出了一种组稀疏贪婪算法GOMP。该方法在成熟的贪婪算法基础上,利用子信道内多频点的组测量信息,根据组测量的概率分布特性来识别宽带信道的活动子信道。这种组测量识别方式使算法能以较少的观测数据实现对宽带信道的快速准确感知,极大地降低了宽带频谱感知所需的采样速率。实验结果表明:该算法比传统的OMP算法及BP算法不仅具有更好的重构效果及频谱检测性能,而且具有更好的压缩性能及实时性能。      

A distributed compressed sensing approach for speech signal denoising

Compressed sensing, a new area of signal processing rising in recent years, seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction. The precondition of compressed sensing theory is the sparsity of signals. In this paper, two methods to estimate the sparsity level of the signal are formulated. And then an approach to estimate the sparsity level directly from the noisy signal is presented. Moreover, a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit (CoSaMP) algorithm. Several simulation results demonstrate the accuracy of the estimated sparsity level and that this denoising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.     

G3-PLC系统压缩感知信道估计的LS-SAMP算法

为了提高G3-PLC系统可靠性,引入压缩感知(Compressed Sensing,CS)的信道估计方法,提出了一种基于最小二乘(Least Squares,LS)的稀疏度预测自适应匹配追踪(LS Based Prediction-Sparsity Adaptive Matching Pursuit,LS-SAMP)算法。该算法在稀疏度自适应匹配追踪(Sparsity Adaptive Matching Pursuit,SAMP)算法的基础上,采用了基于LS的稀疏度预测方法对信道稀疏度进行预估计,使算法可以快速逼近信道的真实稀疏度,提高算法运行效率;其次利用主成分分析方法对观测矩阵进行线性优化,降低观测矩阵相干度,提高算法的重构性能。实验结果表明,在误码率为10-3时,所提算法相对于LS算法的信道估计性能有1 dB的提升,且其运行效率比SAMP算法提升了31.23%,以更有效的方式提高了G3-PLC系统的可靠性。     

块稀疏广义正交匹配追踪算法

基于广义正交匹配追踪,提出了一种在压缩感知框架下,适用于任意块稀疏信号重构的算法。该算法以贪婪迭代为核心,在迭代过程中利用一种新的方法寻找非零块,达到了非零块估计方法优化的目的,提升了算法重构概率。理论分析表明在恰当的受限等距特性常数约束下,该算法能够保证重构原始信号。仿真实验从稀疏度、算法估计步长、测量值数目、迭代次数等方面证明了该算法的有效性与优越性。     

基于加权一致优化的宽带分布式协作压缩频谱感知算法

宽带分布式协作压缩频谱感知不仅降低过高的采样速率,而且改善在低信噪比环境下的频谱感知性能。为进一步提高频谱感知性能,提出一种基于加权一致优化的宽带分布式协作压缩频谱感知算法。该算法根据当前迭代重构出的频谱信号设定下一次迭代重构的权值,促使频谱信号上存在授权用户的子频段产生信号值,降低重构出错的可能性。仿真结果表明,该算法不仅能够增大频谱重构的准确性,而且能够降低感知过程的时间和通信开销,改善频谱感知性能。     

基于压缩感知的超宽带信道估计方法的研究

压缩感知(Compressed Sensing, CS)理论可以从较少的观测样本中恢复稀疏信号。针对超宽带(Ultra- WideBand, UWB)信道的稀疏特性,将压缩感知理论应用于UWB系统的信道估计中,能够有效地降低系统的采样速率。该文针对UWB信道的特点对过完备字典库和观测矩阵进行设计,提出了一种滤波矩阵估计算法。然后,分别利用丹茨格选择器(Dantzig Selector, DS),基追踪降噪(Basis Pursuit De-Noising, BPDN)算法和正交匹配跟踪(Orthogonal Matching Pursuit, OMP)算法实现信号检测,进一步给出UWB信道估计中CS重建算法的选择建议。基于IEEE 802.15.4a信道模型的仿真结果表明,该算法同随机观测算法的检测结果相比,能够在较低的采样速率下获得更好的误码率性能。     

采用时域联合稀疏恢复的多输入多输出水声信道压缩感知估计

多输入多输出(MIMO)水声通信技术可以在极其有限的水声信道频带资源内提高信道容量,但多径和同道干扰的同时存在,使传统信道估计算法如最小二乘算法、压缩感知估计算法的性能急剧下降。考虑到通信数据块间水声信道多径结构存在一定的相关性,该文利用这种数据块间多径结构的时间域相关性建立水声MIMO信道的时域联合稀疏模型,并利用同步正交匹配追踪算法进行多个数据块联合稀疏恢复信道估计,提高MIMO信道多径稀疏位置的检测增益并抑制同道干扰,提高水声MIMO信道的估计性能。仿真和MIMO水声通信海试实验表明了所提方法的有效性。     

基于观测矩阵优化的自适应压缩频谱感知

推导了自适应压缩感知中的重构估算误差,研究了如何降低观测矩阵列向量之间的自相关性,分析了观测矩阵优化对压缩感知重构算法的影响。将观测矩阵优化与压缩感知自适应过程相结合,提出了基于观测矩阵优化的自适应压缩频谱感知算法。仿真结果证实,所提算法比传统算法重构时产生的均方误差(MSE)更低,在同一观测次数下检测概率更高,在达到同等接收操作性能(ROC)时所需观测次数更少。     

Compressed wide spectrum sensing scheme based on BP network

This paper proposes a compressed sensing (CS) scheme to reconstruct and estimate the signals. In this scheme, the framework of CS is used to break the Nyquist sampling limit, making it possible to reconstruct and estimate signals via fewer measurements than that is required traditionally. However, the reconstruction algorithms based on CS are normally non-deterministic polynomial hard (NP-hard) in mathematics, which makes difficulties in obtaining real-time analysis-results. Therefore, a new compressed sensing scheme based on back propagation (BP) neural network is proposed under an assumption that every sub-band is the same. In this new scheme, BP neural network is added into detection process, replacing for signal reconstruction and decision-making. By doing this, heavy calculation cost in reconstruction is moved into pre-training period, which can be done before the real-time analysis, bringing about a sharp reduction in time consuming. For simplify, 1-bit quantification is taken on compressed signals. Simulations demonstrate the performance enhancement in the proposed scheme: compared with normal CS-based scheme, the proposed one presents a much shorter response time as well as a better robustness performance to noise via fewer measurements.     

一种自适应阈值选择的宽带压缩频谱感知重构算法

针对宽带压缩频谱感知算法在未知稀疏度条件下重构频谱效果不理想的问题,提出一种自适应阈值选择的改进型分段正交匹配追踪(Adaptive Threshold Option-Improved Stagewise Orthogonal Matching Pursuit, ATO-IStOMP)算法,该算法根据迭代残差的分布特性自适应地调整原子选择判决门限,使其每次迭代能够高效选择多个原子作为候选集,同时该算法利用残差比阈值对迭代终止条件进行修正,能够实现重构算法的盲停止,增强算法在低信噪比环境下的鲁棒特性。仿真结果表明,ATO-IStOMP算法能够实现对原始信号的盲重构,且在低信噪比环境下的重构性能良好。      

基于部分频谱小波边缘检测的压缩宽带频谱感知

压缩宽带频谱感知是一种采用压缩感知技术去感知相对较宽的频谱的方法.针对压缩宽带频谱感知中信号重构引起的计算资源消耗比较大以及能量检测在低信噪比下感知性能差的问题,提出了基于部分频谱小波边缘检测的压缩宽带频谱感知方法.首先使用基于最大熵准则的部分傅里叶矩阵获得部分频谱,然后根据部分频谱进行小波边缘检测,最后判别.仿真结果证明了该方案的有效性和优异性.     

基于监督学习的协作宽带压缩频谱检测方案

宽带压缩频谱检测存在信号稀疏度未知和次用户检测开销过大的问题.因此,本文提出一种高效的协作宽带压缩频谱检测方案.首先,推导了一种基于学习的稀疏度自适应预测模型.其次,设计了一种宽带频谱筛选算法.最后,提出一种协作宽带压缩频谱检测方案.仿真结果表明,自适应预测模型的拟合效果优于现有预测模型,并且所提检测方案也有效地降低了次用户采样率和频谱重构时延.     

基于压缩感知的加权宽带谱重构算法

压缩感知利用宽带无线信号的频域稀疏特性,能够在低于奈奎斯特速率的采样下利用少量观测数据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,在去噪基追踪算法(BPDN)的基础上提出了一种优化的加权去噪算法(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,且比传统的BPDN和OMP算法具有更好的压缩性能及更小的重构误差;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。      

基于加权平均一致算法的宽带压缩频谱感知

在认知无线电(CR)网络中进行频谱共享接入,首要的任务是进行频谱感知,并发现频谱空洞。基于认知无线网络中信号频域的固有稀疏性,本文结合了压缩感知(CS)技术与加权平均一致(weighted average consensus)算法,建立了分布式宽带压缩频谱感知模型。频谱感知分为两个阶段,在感知阶段,各个CR节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并做出本地频谱估计;在信息融合阶段,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,并得到最终的频谱估计结果,获得分集增益。仿真结果表明,结合压缩感知与加权平均一致算法增强了频谱感知的性能,比在相同的CR网络中使用平均一致算法时有了性能上的提升。      

一种适用于TDOMP算法的测量矩阵优化方法

测量矩阵的优化设计有利于提高压缩感知中信号的重构性能。该文研究了适用于TDOMP (TwoDictionaries OMP)重构算法的测量矩阵优化方法。TDOMP算法是一种改进的OMP算法,该算法使用与感知矩阵互相关性低的匹配矩阵来辨识正确的感知矩阵原子。所提方法利用交替投影的思想来优化测量矩阵从而得到相关性低的感知矩阵和匹配矩阵,然后用于TDOMP算法来提高信号的重建性能。仿真实验验证了所提方法的有效性。      

基于随机滤波的探地雷达成像方法

探地雷达是一种超宽带雷达系统,若按传统的奈奎斯特采样,雷达回波信号需要大量空间存储。压缩感知可以实现利用少量的测量值对稀疏信号进行重构,其中最为关键的是测量矩阵和重构算法的选择。本文将压缩感知应用于探地雷达成像,并利用随机滤波的思想选择测量矩阵,可以有效减少测量矩阵中非零值的个数。利用正交匹配追踪算法对信号进行重构,算法简单,降低了数据的存储量和运算复杂度,该算法同样可以对时间和空间上同时压缩的数据进行成像。最后,本文给出基于时间连续信号的GPR接收机一种CS实现方案。仿真结果表明,本文提出的成像方法可以以少量数据精确地对信号进行重构,并且运算量少。      

毫米波系统中基于压缩感知的波束空间信道估计

波束空间信道估计中大多为窄带信道模型,因此,将基于天线开关网络(Antenna Switching Network, ASN)的导频分组方案应用到宽带信道中,将信道估计构建为稀疏信号恢复问题。针对稀疏信号重构算法中基于广度优先准则的多径匹配追踪算法(Breadth First Based Multipath Matching Pursuit, MMP-BF)需要已知稀疏度的问题,使用分段弱正交匹配追踪算法(Stagewise Weak Orthogonal Matching Pursuit, SWOMP)的阈值准则代替MMP-BF算法的相关性准则,并在每一次迭代结束后根据估计值再次采用阈值准则进行回溯以提高原子选择的准确性。仿真结果表明,在信噪比为0 dB时,改进算法在低导频开销下相比正交匹配追踪(Orthogonal Matching Pursuit, OMP)等传统算法估计精度提高了至少2 dB,相比MMP-BF算法在不同路径个数下具有更好的鲁棒性。     

SNR-based weighted cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing (CSS) is an approach to confront fading environment. However, in conventional cooperative spectrum sensing (CCSS), the difference among the secondary users (SUS) is ignored when SUS suffer from different fading. In this article, a signal-to-noise ratio (SNR)-based weighted spectrum sensing scheme is proposed to improve the sensing performance. The sensing performance of the weighted spectrum sensing scheme is then derived. Considering the minor contribution of the SUS with small weighted factor, a selective CSS scheme is proposed, where SUS with low SNR are not selected into cooperative spectrum sensing. The simulation results confirm the analytical results. And the performance of weighted scheme is better than that of conventional schemes. In the case where the SNR of SUS are randomly distributed, the performance of selective scheme is almost the same as the weighted scheme while the number of cooperative SUS is reduced to save the consumption of system resource in cooperation with little additional complexity.     

A sparsity and compression ratio joint adjustment method for collaborative spectrum sensing

Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.