认知无线网络中硬合并协同频谱感知的融合准则优化

频谱感知是认知无线电一项基础的任务。在认知无线网络中,多个次级用户可以协同工作,对主用户进行可靠感知。如何融合多个次级用户的感知信息是实施协同感知的关键。本文围绕集中式认知无线网络中的硬合并协同感知技术展开研究,讨论了常用的k-out-of-m融合准则。与OR准则（k=1）、Half-voting准则（k=m/2）和AND准则（k=m）等特例不同,本文考虑参数k任意取值的场景,从最小化贝叶斯代价的角度,推导出了最优k值的闭合表达式。仿真结果验证了该闭合公式的有效性,并显示最优k值随判决门限、频谱非空闲与空闲先验概率比、漏检与虚警影响因子比的增大而减小,且在信噪比场景中更具应用价值。      

Energy Efficiency in Cognitive Radio Network Using Cooperative Spectrum Sensing

Wireless Personal Communications - In this paper, we consider an optimization of number of secondary users (SUs) in a cooperative spectrum sensing by maximizing the energy efficiency of the...     

协作频谱感知在认知网络中的应用

认知无线电能够灵敏感知周围环境的变化,通过频谱感知功能发现频谱空洞,使得认知无线电能够与周围通信环境相适应。在认知无线电网络中,认知用户（次用户）必须连续地监控频谱,检测主用户是否存在。如果主用户存在,次用户必须尽快地退出该频段,从而不影响主用户。在文中采用一种协作机制,即两用户进行协作频谱感知,从而提高主用户的检测率,减少了检测时间,并且得到捷变增益。我们用Matlab给出仿真结果。     

Cooperative Spectrum Sensing Techniques in Cognitive Radio

Cognitive radio has become an effective theory to solve the inefficiency of the spectrum usage. One of the main requirements of cognitive radio systems is the ability to reliably detect the spectrum hole. Previous works on the problem of detection for cognitive radio have suggested the necessity of user cooperation to enable the detection at the low signal-to-noise ratios experienced in practical situations. This paper introduces energy sensing and the cooperative sensing techniques: AND model, OR model, counting model, double threshold model, likelihood ratio model, linear cooperation model and DWCS model. It is proved that significant cooperative gain can be achieved by the proposed models in reducing interference and improving spectrum usage.     

认知无线电的合作频谱检测分析

认知用户需要持续快速地检测主用户的出现,对基于认知无线电的合作频谱检测机制进行了分析。在理想的两用户网络中,认知用户采用合作机制可以提高系统的平均侦测时间。在多认知用户网络中,参与合作的用户越多,单个认知用户对主用户的侦测概率越高。对于多用户网络,还考虑了整体的实现。     

认知无线电网络的一种协作频谱感知方案

认知无线电技术能够让非授权用户利用已经分配给授权用户的频段.为了不对首要用户的工作造成干扰,认知用户需要对频谱进行不间断的监测来判断首要用户是否存在.因此,频谱的感知是认知无线电技术的关键.协作频谱感知能够充分的利用网络资源,提高网络中的认知用户的检测概率.文中笔者简单地介绍了一种协作频谱感知的方案.仿真结果表明,通过该方法能够提高网络中认知用户的检测概率,提高网络的检测灵敏度.     

基于信噪比加权的协作频谱感知方法

认知无线电中协作频谱感知技术能显著提高频谱感知性能,但传统协作频谱感知算法大多采用等权重进行数据融合,未考虑在实际通信环境中感知用户的信噪比差异。针对这种情况,提出了一种基于信噪比加权的协作感知方法,根据信噪比影响因子的不同,给感知用户本地判决结果分配不同的权重,并在此基础上对影响因子定义做了改进,减小了信噪比极值对感知性能的影响。仿真结果表明,在AWGN信道非衰落环境中,基于信噪比加权的协作感知比传统协作感知性能要好,改进后的加权感知方法对感知性能又有进一步的改善。     

多天线感知无线电中的协作频谱感知算法

不同于以往单天线感知用户的频谱感知研究,该文提出多天线情形下基于最优功率分配和协作分集的频谱感知算法。根据信道条件利用奇异值分解(SVD)在每根天线上进行功率注水,并考虑多天线情况下两用户网络协作频谱感知授权用户的判决检测过程,利用基于指数衰减的路径损耗模型,分析多天线感知网络的各态历经容量和检测授权用户的概率及时间。理论分析及仿真结果表明,多天线感知用户通过最优功率分配和协作频谱感知不仅使感知网络具有最大的各态历经容量,同时改善系统的检测性能。     

认知无线电合作频谱感知算法研究

频谱感知是实现认知无线电功能的前提条件,也是认知无线电领域的一个研究热点。近年来人们提出了很多频谱感知的方法,尤其是合作频谱感知算法日益受到人们的关注。综述了合作频谱感知技术的最新研究进展,从简单的线性信息融合算法到复杂的非线性感知算法,以及多天线合作感知等算法,通过仿真比较各种方法的检测性能,并展望未来频谱感知所面临的挑战和发展趋势。     

Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In order to solve the uncertainty resulting from shadowing effect and resist the attack from malicious cognitive radio (CR) users, we propose a fault-tolerant cooperative spectrum sensing scheme for CR networks, where an energy detection-based local spectrum sensing is performed at each CR user, a coefficient is used to weight each CR user’s sensing result, a linear weighted fusion process is performed at the fusion center (FC) to combine received sensing results. For a fault-tolerant cooperative spectrum sensing scheme, the most important issue is to distinguish whether the CR user is reliable or not. In this paper, a reputation-based cooperative mechanism is presented to alleviate the influence of the unreliable sensing results from CR users suffering shadowing and the false sensing data from malicious CR users on the detection result at the FC. In proposed fault-tolerant cooperative scheme, each cooperative CR user has a reputation degree which is initialized and adjusted by the FC and used to weight the sensing result from the corresponding user in the fusion process at the FC. And then, two reputation degree adjusting methods are presented to manage the reputation degree of each CR user. Simulation results show that the proposed scheme can not only weaken the harmful influence caused by malicious CR users, but also alleviate the corrupted detection problem resulting from destructive channel condition between the primary transmitter and the CR user. Moreover, the detection performance of the fault-tolerant cooperative scheme, which has a feasible computational complexity and needs no instantaneous SNRs, is close to that of the optimal scheme.     

Cooperative Spectrum Sensing in Cognitive Radio Using Bayesian Updating with Multiple Observations

We study cooperative spectrum sensing in cognitive radio (CR) networks using the hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel operates in a time division multiple address (TDMA) manner. Thus, spectrum sensing is operating in a slot-by-slot basis. In contrast to the conventional Bayesian updating using only one observation, in this work, we propose to perform the update in a concatenated fashion with all the observations available from the secondary users (SUs). In the proposed scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed concatenated update scheme with that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of the further increase in the number of SUs are limited.     

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。     

认知无线电协作频谱感知技术综述

频谱感知是实现认知无线电功能的前提条件,也是认知无线电领域的一个研究热点.近年来人们提出了很多种频谱感知方法,尤其协作感知技术日益受到关注.综述了协作频谱感知技术的最新研究进展,先描述典型的认知无线电协作频谱感知模型,然后讨论了协作感知中信息融合及性能分析等关键问题,最后指出了协作感知的研究挑战和发展趋势.     

Non-uniform Quantized Data Fusion Rule for Data Rate Saving and Reducing Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

Wireless Personal Communications - In this paper a pretty new concept of non-uniform quantized data fusion (N-QDF) rule reducing control channel data overhead has been proposed for energy detection...     

基于SNR比较的认知无线电协作频谱检测

协作频谱检测是认知无线电中的一项关键技术,其检测性能取决于感知节点的本地频谱检测结果的可靠性,而目前已有的方案中对此却很少考虑.对此,认为不同的认知节点信噪比(SNR)导致了各节点本地检测结果的可靠性不同,故在此基础上提出了一种基于融合中心进行SNR比较的认知无线电协作频谱检测算法.在该算法中各认知节点将本地的判决结果和估计的SNR同时发送到融合中心,然后在融合中心对SNR进行比较,按照文中设计的规则来选取有较好SNR的认知节点参与判决融合.数值分析和计算机仿真表明,该方法能有效提高检测概率,并减少参与判决融合的节点数量.     

Fusion Rules Effects on Lifetime Maximization of Multi-channel Cooperative Spectrum Sensing

Cooperative spectrum sensing (CSS) is an efficient method to detect the reliability of the free spectrum, however, with great overhead and energy consumption. In this paper, we study a much broader application of CSS, where the sensors are used to sense multiple channels. On the one hand, since the tiny and low-cost sensors do not have high-speed analog-to-digital-convertors, they cannot simultaneously sense more than one channel. Therefore, the simultaneous multi-channel CSS is an important issue in a cognitive sensor network (CSN). On the other hand, these tiny sensors do not have high-power batteries, which makes the network lifetime as an important metric. In this paper, node selection is proposed for the multi-channel CSS to maximize the lifetime of a CSN under some detection constraints with lower overhead than cooperative sensing by all the sensors simultaneously. We analyze the problem for the OR and the AND rules, which can be implemented at the fusion center. The problem is solved by using convex optimization methods where assignment indices for every sensor are assumed. We provide a performance analysis through simulations using MATLAB, which shows that the sensor selection scheme provides a significant long lifetime for a CSN compared to the case where sensors are selected randomly and where all sensors are just classified to sense the channels simultaneously.

     

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.     

认知无线电中基于加权硬判决的协作频谱感知算法

Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions.If the energy value falls into the corresponding region, it will be judged as “1”, no information or “0”. When the probability of false alarm is constrained to be constant, the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination (EGC) scheme. Moreover, compared with the traditional one bit hard combination, fewer average sensing bits are required to transmit to the data fusion center with the proposed method.     

Optimization of Cluster-Based Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks with Soft Data Fusion

This paper investigates cluster-based cooperative spectrum sensing issues in two-layer hierarchical cognitive radio networks with soft data fusion. We first define a two-phase reporting protocol in the paper. In the first phase, secondary users forward their soft sensing information to cluster heads (CHs) over large-scale fading. In the second phase, all CHs transmit the aggregated soft energy information to the fusion center (FC) with different weights. Thus we derive the network false alarm (FA) and the detection probabilities as functions of the FC decision threshold, the clustering algorithm and different weights. Given a target on the detection probability, minimizing the FA probability is then formulated as a constraint optimization problem within two scenarios including additive white Gaussian noise environment and Rayleigh fading environment. A close-form upper bound of the FA probability is derived and a novel clustering scheme is also proposed for each scenario. Numerical results show that the proposed schemes achieve a satisfying performance.     

Spatial-Temporal Cooperative Spectrum Sensing in Flat Fading Channels for Cognitive Radio Using Extend Kalman Filter

This paper investigates a new channel gain map tracking by Space-Time Extended Kalman Filtering (STEKF) for a flat channel, and a novel spectrum sensing via Time Spatial Weighted Non-negative Lasso (TSWNL) algorithm. STEKF enables CRs to estimate and interpolate channel gain map for the entire geographical area of interest with a limited number of CRs measurements. In order to sense primary users (PU) activities, include the transmission power by each PU, location and number of active PUs, TSWNL algorithm is proposed. Numerical results illustrate that the proposed STEKF channel estimation and TSWNL sensing algorithms outperforms linear methods.