Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks

Recently, cognitive radio (CR) access has received much attention to overcome spectrum scarcity problem. Spectrum sensing methods are often used for finding free channels to be used by CR. In this paper, the problem of cooperative spectrum sensing will be investigated in CR networks over realistic channels. This problem is not clarified until now by taking into account the error effect on the decision reporting. The analytical expressions of the hard and softened one bit and two bits hard combination scheme for cooperative spectrum sensing will be derived. These expressions are investigated to compare with simulation results. The analysis and simulation results show that the performance of cooperative spectrum sensing is limited by the probability of reporting errors. Moreover, it is shown that there is a significant performance loss when a final decision regarding to primary user’s (PU) state made at the fusion depends on a set of local spectrum sensing information that are distorted by imperfect reporting channels during transmission. The probability of detection is degraded due to imperfect reporting channel by 16.5% and 12.2% with one bit hard combination and softened two bits hard combination, respectively. To reduce this performance loss, Amplify and Forward (AAF) relying mechanism will be proposed. The probability of detection is improved by 8% and 9.3% with one bit hard combination and softened two bits hard combination, respectively using AAF relaying mechanism.     

认知无线电中基于感知信息量化的合作频谱感知

针对认知无线电频谱感知性能的提高与传输开销的矛盾,本文提出了一种基于感知信息量化的合作频谱感知方案。该方法对各认知用户的本地检测结果采用3个判决门限进行2比特位的量化,在融合中心处对量化信息加权处理后,得到最终主用户是否存在的判决。本文详细研究了采用该方案的检测概率,虚警概率和吞吐量,理论分析和仿真结果表明,该方法在每个认知用户只增加1bit传输开销的前提下能够极大的改善检测性能,实现了检测性能与传输开销的较好权衡。     

Adaptive Threshold Based Energy Detection over Rayleigh Fading Channel

Spectrum sensing is one of the most important affair in the implementation of cognitive radio networks. Energy detection is the widely used spectrum sensing algorithm due to its ease of implementation and non-requirement of any prior information about primary users. However, performance of conventional energy detection scheme deteriorates in the fading environment. Performance of the detector also declines because of the inappropriate setting of decision threshold. In this context, here an adaptive threshold based energy detection scheme is proposed to find the optimal value of threshold for energy detection method over Rayleigh fading channel. A closed-form expression for average probability of detection over Rayleigh channel is derived and thereafter optimal value of threshold is found by Golden section search algorithm. Results demonstrates that proposed scheme performs better than conventional fixed threshold energy detection schemes. For example, our proposal yields a 34% decline in the probability of error at ??25 dB SNR in Rayleigh fading environment.

     

Double‐threshold cooperative detection for cognitive radio based on weighing

In cognitive radio (CR), the single‐threshold cooperative detection is sensitive to the noise power. In this paper, a double‐threshold cooperative detection scheme based on weighed combination is proposed to improve the global detected performance of the cooperative detection. The double thresholds that are decided by the restriction probability are adopted to divide the energy statistics observed by CRs into three subareas, and the detected performance of the CRs locating in the confusion area is fortified through the weighed data fusion by the coordinator. The CRs in the other two areas can make local binary decisions because of their exact sensing performance and then forward their 1‐bit decisions to the coordinator that will combine all the received decisions to give the final decision on the presence of the primary user. In this paper, both the periodic single‐band and wideband cooperative detections are investigated, and the probabilities of spectrum utilization by these two patterns are respectively analyzed. The simulation shows that the detection probability of the proposed scheme is better than that of the single‐threshold cooperative detection with decision fusion but slightly worse than that of the single‐threshold cooperative detection with data fusion. However, the average number of the bits sent to the coordinator by the proposed algorithm is less than that by the data fusion, and it may increase by the decreasing of the restriction probability. The simulation also shows that the periodic wideband sensing can obtain higher spectrum utilization than the periodic single‐band sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

一种面向多波段数字遥感影像的版权保护方案

针对数字遥感影像大尺度、多波段、高保真的特性,提出了一种新颖的版权保护方案.理论分析了第一奇异值向量方向的稳定性;通过比较某一参考向量分别与两个选定数据块的第一奇异值向量的夹角关系,建立单波段图像水印特征;然后为遥感影像的多波段特性提出了水印检测的快速策略与一般策略.实验表明该方案对波段攻击与灰度攻击、组合攻击等保持图像内容的操作具有较强的鲁棒性.     

Novel quantization‐based spectrum sensing scheme under imperfect reporting channel and false reports

In this paper, a novel sensing scheme, uniform quantization for cooperative sensing (UniQCS), that employs a uniform quantizer is proposed. UniQCS is based on energy detection and uses weight vector for global decision function. It performs better than hard decision algorithms such as majority and k‐out‐of‐n in terms of probability of detection and false alarm at the cost of a marginal increase in overhead bits under imperfect reporting channel and false reports. The probability of detection is maximized for a given probability of false alarm constraint by the proposed method. For detailed analysis, the UniQCS is compared with equal gain combiner scheme, which performs far better than hard decision algorithms, via highest bandwidth requirement. The proposed algorithm performs close to equal gain combiner. Moreover, the robustness of UniQCS to sensing error is analyzed when some nodes always report false decisions to the fusion center and the reporting channel is imperfect. For probability of false alarm equal to 0.01, performance gain of UniQCS is at least 45% compared with the other methods when there are two false reporting nodes. UniQCS performance gain is at least 15% compared with other methods for probability of reporting channel error equal to 0.001. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal multi-threshold quantization scheme for bioinformatics inspired cooperative spectrum sensing in cognitive radio networks

Hard decision combination provides bandwidth efficiency but the results produced are unreliable while soft decision combination has better results but at the expense of much consumption of bandwidth. An acceptable trade-off is achieved between the two in case of quantized decision combination. In this paper an optimal quantization scheme is proposed in which the local sensing information is quantized so the maximum detection probability is met while the false alarm probability remains under a certain constraint. The proposed optimal scheme works on the basis of energy detection and the local quantization thresholds are found through iterative search. Smith–Waterman algorithm (SWA) is used to compare the local sensing reports of the CR users and similarity indexes are found for the CR users. The local sensing decisions of the CR users below a certain calculated threshold are rejected and are not included in the final decision combination at the FC. For detailed analysis, SWA-based rules of decision combination with optimal quantization thresholds are compared with a scheme that employs SWA-based rules of decision combination with heuristically selected quantization thresholds and a conventional majority combination scheme. Simulation results show that the proposed scheme performs better than the other two schemes.     

Energy efficient cooperative spectrum sensing in wireless multi-antenna sensor network

This paper will address sensor selection problem for spectrum sensing in a cognitive radio network. The sensor’s limited energy is an important issue which has attracted more attention in recent years. An energy efficient cooperative spectrum sensing will hereby be proposed when multi-antenna sensors are used. Two decision-making techniques are utilized for the combination of antennas’ signals in each sensor: hard and soft decision-making. OR rule is used for hard decision-making technique while selection combining, equal gain combining and maximum ratio combining (MRC) are used for the soft one. In each combination scheme, the sensor selection is a problem by means of which both the energy consumption is minimized and the detection performance gets satisfied. The problem is solved based on the standard convex optimization method. Simulation results show the achievement of a significant energy saving compared to the networks using single-antenna sensors specifically in low signal to noise ratio state. Among all methods, MRC combining enjoys the least energy consumption, as well; it satisfies the desired detection performance.     

Optimal Energy Harvesting-based Weighed Cooperative Spectrum Sensing in Cognitive Radio Network

In cognitive radio (CR) network, to improve spectrum sensing performance to primary user (PU) and decrease energy wastage of secondary user (SU) in cooperative spectrum sensing, an energy harvesting-based weighed cooperative spectrum sensing is proposed in this paper. The SU harvests the radio frequency (RF) energy of the PU signal and then converts the RF energy into the electric energy to supply the power used for energy detection and cooperation. The time switching model and power splitting model are developed to realize the notion. In the time switching model, the SU performs either spectrum sensing or energy harvesting at any time, while in the power splitting model, the received PU signal is split into two signal streams, one for spectrum sensing and the other one for energy harvesting. A joint optimization problem is formulated to maximize the spectrum access probability of the SU by jointly optimizing sensing time, number of cooperative SUs and splitting factor. The simulation results have shown that compared to the traditional cooperative spectrum sensing, the proposed energy harvesting-based weighed cooperative spectrum sensing can decrease the energy wastage obviously while guaranteeing the maximum spectrum access probability.     

基于信号能量分布拟合优度的长短时记忆网络频谱感知算法研究

传统频谱感知算法性能在低信噪比下不够理想,在高信噪比下较好,算法性能随信噪比降低逐渐变差。本文提出了基于信号能量分布拟合优度的长短时记忆网络频谱感知算法,利用授权用户信号存在时的接收信号为基础,计算接收信号的能量分布,并将通过拟合优度算法得到的距离值作为特征构造特征向量,然后将特征向量输入长短时记忆网络训练得到模型,最后将测试数据输入训练模型进行预测,从而实现频谱感知。仿真结果表明,本文提出的新算法在信噪比为-13 dB,采样点数为28时,检测概率达到96.21%,明显优于传统能量检测算法和传统拟合优度算法。