基于Friedman检验的非参数协作频谱感知方法

协方差矩阵频谱感知方法在天线相关性低时感知性能较差,该文针对这一问题提出一种基于Friedman检验的非参数协作频谱感知方法。分布式放置的感知节点具有空间分集的特性,因此在同一时刻感知节点上的信号功率不完全相同。利用这一特点,提出通过比较各感知节点的信号功率水平来实现频谱感知。由于采用了非参数化表示,该方法对噪声不确定性稳定,且适用于任意统计分布的噪声。另外,推导了所提方法判决门限的理论表达式,结果显示判决门限与采样点数无关,因此在采样点数变化的情况下无需重新设置判决门限。仿真结果验证了上述理论分析的有效性。     

认知无线电中协作压缩频谱感知算法

针对宽带频谱认知无线电环境中,传统能量检测法在信噪比较低时,容易出现误检而使系统的检测性能下降的问题,文中提出了一种新型高性能的协作频谱感知算法,它是基于压缩理论的多节点频谱感知方法,各节点之间采用基于双判决门限的协作方式。仿真结果显示,双门限协作压缩频谱感知算法在低信噪比的情况下,检测性能明显优于传统能量检测法。     

最优认知用户配对与协作感知算法研究

针对协作算法频谱感知和时隙消耗方面的不足,提出一种最优用户配对与协作感知算法。采用接收信噪比最优的认知用户为感知用户且以接收信噪比次优的认知用户为其中继的配对准则,获得最佳频谱感知性能。通过优化设置协作门限,选择性地采用非协作或协作模式,获得更低的时隙消耗。同时,推导了在瑞利平坦衰落环境下该算法的频谱检测概率下界与时隙消耗。数值计算结果表明,与原有算法相比,该算法不仅检测概率更高且时隙消耗更低。     

多天线认知系统中最优协作用户数的选择研究

针对非理想控制信道传输错误对认知系统中多天线多用户协作频谱感知性能影响的问题,提出一种使得认知系统误检概率最小的最优协作用户数选择方案,并推导给出其闭式表达式。该方案首先根据单根天线错误检测概率最小的原则推导出认知用户单根天线最优的判决门限。然后利用“K秩”准则对多天线进行合并,根据认知用户错误检测概率最小的原则推导出最优的“K”值。最后根据认知系统误检概率最小的原则推导出最优的协作感知用户数。通过仿真验证了该方案理论的正确性,并分析给出了控制信道错误概率对认知系统检测性能的影响。相比于传统的协作检测算法,本方案具有更好的检测性能。      

认知无线网络中动态双门限协作频谱感知性能的优化方案

频谱感知作为认知无线电的关键技术,得到广泛深入的研究。其中衡量协作频谱感知性能的主要参数为全局虚警概率和全局漏检概率,它们之和被定义为全局错误概率。本文研究基于双门限能量检测的协作频谱感知性能的优化方案,首先,固定双门限能量检测的检测门限值,对表决融合准则的投票门限进行优化,使得在该能量检测门限值条件下,协作频谱感知的全局错误概率最小;然后在表决融合准则的投票门限取最优值的前提下,对双门限能量检测的检测门限值进行了优化,在不同接收信噪比条件下,最优的检测门限值是动态的,所以要根据信噪比确定最优的检测门限值,使得协作频谱感知的全局错误概率在各信噪比条件下都达到最小值,从而提高了协作频谱感知的性能。仿真结果表明,表决融合准则的投票门限和双门限能量检测的检测门限值取得各自的最优值时,全局错误概率最低,检测性能最好。      

分段LFM调制随机波形反侦察设计

频谱感知通过搜索空间中的空闲频段或干扰较少的频段,为未授权用户提供频谱接入的机会,是认知雷达中的一项关键技术。众所周知,协作感知可以提高频谱感知的精度,但是往往需要设置专门的控制信道来汇报协作节点的本地感知结果。这种汇报方式不利于节约信道资源。为了解决这一难题,提出了一种直接利用感知信道进行感知结果汇报的协作感知机制。该机制通过本地感知和协作感知的自适应切换及汇报节点的自适应功率控制,不仅提高了频谱感知的精度,而且节约了汇报信道资源。最后,通过仿真实验验证了该机制的有效性。文中提出的协作感知机制对认知雷达系统的设计具有重要的理论和实践意义。     

动态频谱接入中基于最小贝叶斯风险的稳健频谱预测

针对频谱感知错误累积造成频谱预测性能恶化问题,该文提出一种基于最小贝叶斯风险的稳健频谱预测策略。分布拟合检验表明频谱预测输出服从正态分布,定义频谱预测输出的贝叶斯风险函数,证明使贝叶斯风险函数最小的频谱预测输出判决门限将使频谱预测的均方误差最小,求得了使贝叶斯风险最小的最优判决门限,构建稳健频谱预测策略。仿真结果表明,与固定判决门限的神经网络频谱预测相比,稳健频谱预测策略改进了频谱感知错误下的频谱预测性能,改善了非授权用户的动态频谱接入性能。     

感知不确定下卫星认知无线网络多频谱接入优化策略

针对卫星认知无线网络频谱感知不确定性较大导致传统频谱接入机制效率降低的问题,该文提出一种基于动态多频谱感知的信道接入优化策略。认知LEO卫星根据频谱检测概率与授权用户干扰门限之间的关系,实时调整不同频谱感知结果下的信道接入概率。在此基础上以系统吞吐量最大化为目标,设计了一种基于频谱检测概率和虚警概率联合优化的判决门限选取策略,并推导了最佳感知频谱数量。仿真结果表明,认知用户能够在不大于授权用户最大干扰门限的前提下,根据授权信道空闲状态动态选择最佳频谱感知策略,且在检测信号信噪比较低时以更加积极的方式接入授权频谱,降低了频谱感知不确定性对信道接入效率的影响,提高了认知系统吞吐量。     

一种认知无线网络中的协作感知优化方法

认知无线电技术能够显著提高频谱资源的利用率,因此越来越受到学术界的关注。频谱感知是认知无线电的基础,一方面它要求能够快速找到可用频谱,另一方面当授权用户返回时又要求能够快速释放信道避免干扰。提出了一种认知无线网络中多用户协作感知分级方法,并通过数学推导给出最优的分级策略。仿真结果表明,采用所提出的协作频谱感知方法,能够提高认知无线网络的吞吐量。     

基于最优中继的自适应协作频谱感知算法

针对认知系统中感知信道存在衰落和中继能耗较大问题,提出一种兼顾感知性能和感知能耗的中继协作频谱感知算法。该算法通过机会中继协作、基于效益函数的最优中继协作和系统参数自适应调整机制,能够获得性能与能耗的优化折中。文章对所提算法进行了详细的理论推导和性能分析,并对非中继协作感知算法和最优中继固定的协作感知算法以及本算法在不同系统参数下的感知性能进行了仿真比较。结果表明本算法具有一定的优越性。     

认知无线电最优线性协作宽带频谱感知

本文提出了基于最优线性协作的宽带频谱感知方案。通过次级用户之间的协作,为认知网络的频谱感知提供分集,利用融合中心融合多个次级用户的宽带频谱感知数据来获取最优权重,并生成全局判决统计量,最终使用全局阈值完成最后的检测判决。由于所提出的两种协作宽带感知方案或需要精确地估计授权用户的信号强度和噪声方差,或感知性能不足,因而,还提出一种更易实现的方案。理论分析和仿真结果表明,本文所提出的协作感知方案可以有效地提高频谱感知性能,并且性能优于传统等增益合并方案。      

Distance criterion-based quantizer design for cooperative spectrum sensing

In terms of sensing node’s energy and reporting channel’s bandwidth constrains problem for cooperative spectrum sensing in cognitive radio networks,an optimal quantizer design method based on distance criterion was proposed.First of all,the Bhattacharyya distance of received quantized data at the fusion center (FC) was calculated as performance criteria,the optimization mathematical model of the quantizer was constructed,and the optimum quantization thresholds were obtained by using particle swarm optimization algorithm.According to received sensing nodes’ quantized data at the FC,a log-likelihood ratio detector was constructed to decide the presence or absence of primary user signal,the upper bound to sensing performance of energy detector that without quantization was derived.Compared with the existing methods in literatures,the performance of proposed 3-bit quantization method approaches to the upper bound performance of energy detector,under the premise of obtaining comparable detection performance,the requirement of communication bandwidth is reduced.     

Cooperative spectrum sensing using amplify-and-forward relaying with partial relay selection in cognitive radio networks

Cooperative spectrum sensing has been shown to be an effective approach to improve the detection performance by exploiting the spatial diversity among multiple cognitive nodes. By using the amplify-and-forward relaying with partial relay selection, this paper proposes a novel cooperative spectrum sensing scheme, which provides higher detection performance and is interesting in distributed cognitive radio networks. In the proposed sensing scheme, the “best” cognitive relay by means of partial relay selection technique amplifies and forwards the signals transmitted from the primary user (PU) to the cognitive user (CU). Then the CU detects PU’s states (i.e., presence or absence) via an energy detector. Moreover, the average missed-detection probability of proposed sensing scheme is studied over Nakagami-m fading channels, where m is a positive integer. In particular, the tight closed-form lower bounds of the average missed-detection probability are presented for the convenience of performance evaluation in practice. Finally, numerical results are provided to validate the derived closed-form lower bounds and the influence of the number of cognitive relays on the detection performance is also discussed.     

认知无线电中一种感知节点集自适应选择算法

本文主要考虑认知网络中感知节点集的选择问题。联合谱感知技术虽然可以极大地提高认知系统的感知性能,但是随着参与感知的认知节点数目的增加,对系统资源的占用也会越来越多,使系统的传输效率下降。本文首先给出了认知网络中最优感知节点集的概念,接着分析了最优感知节点集的节点数目和平均接收信噪比所必须满足的条件,最后通过推导得到了在固定虚警概率条件下最优感知节点集的检测概率与它的节点数目和平均接收信噪比之间的关系表达式,并在此基础上提出了一种最优感知节点集的自适应选择算法。该算法不但能在认知网络中寻找最优感知节点集,同时还可以适应认知网络的动态拓扑变化。仿真结果证明了该算法的有效性。      

Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

The optimal resource allocation in MIMO cognitive radio networks with heterogeneous secondary users, centralized and distributed users, is investigated in this work. The core aim of this work is to study the joint problems of transmission time and power allocation in a MIMO cognitive radio scenario. The optimization objective is to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. At first, the joint problems of transmission time and power allocation for centralized SUs in uplink is optimized. Afterwards, for the heterogeneous case with both the centralized and distributed secondary users, the resource allocation problem is formulated and an iterative power water-filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. A dynamic optimal joint transmission time and power allocation scheme for heterogeneous cognitive radio networks is proposed. The simulation results illustrate the performance of the proposed scheme and its superiority over other power control schemes.     

Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates linear soft combination schemes for cooperative spectrum sensing in cognitive radio networks. We propose two weight-setting strategies under different basic optimality criteria to improve the overall sensing performance in the network. The corresponding optimal weights are derived, which are determined by the noise power levels and the received primary user signal energies of multiple cooperative secondary users in the network. However, to obtain the instantaneous measurement of these noise power levels and primary user signal energies with high accuracy is extremely challenging. It can even be infeasible in practical implementations under a low signal-to-noise ratio regime. We therefore propose reference data matrices to scavenge the indispensable information of primary user signal energies and noise power levels for setting the proposed combining weights adaptively by keeping records of the most recent spectrum observations. Analyses and simulation results demonstrate that the proposed linear soft combination schemes outperform the conventional maximal ratio combination and equal gain combination schemes and yield significant performance improvements in spectrum sensing.     

Smart antenna‐assisted spectrum sensing for robust detection in cognitive radio networks

In this paper, we consider the problem of multiband spectrum sensing by employing smart antenna arrays at the cognitive receiver. Although energy detection is widely used for spectrum sensing in cognitive radio networks because of its simplicity and accuracy, it is severely deteriorated by the noise uncertainty. This paper introduces robust spectrum sensing techniques to circumvent this difficulty, which operate simultaneously over the total frequency channels rather than a single channel each time. To enhance the detection performance, the proposed schemes jointly utilize the information of eigenvalues and eigenvectors, signal and noise subspace components in conjunction with the likelihood functions and Gerschgorin radii. Neither subjective decision threshold setting nor the estimation of noise power is required in our schemes, making them robust to noise uncertainty. Simulations are presented to validate the performance of the proposed schemes, and the results show that our schemes can outperform other existing spectrum sensing methods. Copyright © 2014 John Wiley & Sons, Ltd.     

A Novel Two-Stage Entropy-Based Robust Cooperative Spectrum Sensing Scheme with Two-Bit Decision in Cognitive Radio

Spectrum sensing is a key problem in cognitive radio. However, traditional detectors become ineffective when noise uncertainty is severe. It is shown that the entropy of Gauss white noise is constant in the frequency domain, and a robust detector based on the entropy of spectrum amplitude was proposed. In this paper a novel detector is proposed based on the entropy of spectrum power density, and its performance is better than the previous scheme with less computational complexity. Furthermore, to improve the reliability of the detection, a two-stage entropy-based cooperative spectrum sensing scheme using two-bit decision is proposed, and simulation results show its superior performance with relatively low computational complexity.     

Optimization of Time-Domain Combining Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio networks, the secondary users take chances to access the spectrum without causing interference to the primary users so that the spectrum access is dynamic and somewhat opportunistic. Therefore, spectrum sensing is of significant importance. In this paper, we propose a novel time-domain combining cooperative spectrum sensing framework, in which the time consumed by reporting for one secondary user is also utilized for other secondary users’ sensing. We focus on the optimal sensing settings of the proposed sensing scheme to maximize the secondary users’ throughput and minimize the average sensing error probability under the constraint that the primary users are sufficiently protected. Some simple algorithms are also derived to calculate the optimal solutions. Simulation results show that fundamental improvement of the achievable throughput and sensing performance can be obtained by optimal sensing settings. In addition, our proposed scheme outperforms the general frame structure on either achievable throughput or the performance of average sensing error probability.     

全双工中继协作下的认知MIMO系统吞吐量最大化研究

本文研究了全双工中继协作下的认知MIMO系统的平均吞吐量最大化问题。与传统的中继协作认知无线电系统不同的是,该系统模型中的双工中继节点既能协助认知用户源节点进行多天线频谱感知以提高频谱检测性能,也能解码转发认知用户源节点的发送信号以获得更大的系统吞吐量。为使系统平均吞吐量最大,首先,本文以认知用户能获得的最大平均频谱空洞被发现的概率为目标,对系统的帧结构进行优化以获得最佳的感知时间,接着对多个发送天线进行优化以选择出最佳的发送天线,并推导出了在总的发送功率和对主用户干扰受限条件下的认知用户源节点和双工中继节点的最佳功率分配方案。最后的仿真结果表明本文提出的系统模型和优化方案相比传统的双工等功率分配方案以及单工功率分配方案能够获得更大的系统平均吞吐量。