Centralized Cooperative Spectrum Sensing Research Articles

Optimized Cluster-Based Cooperative Spectrum Sensing Over Weibull, Nakagami, Rician, and Rayleigh Fading Channels

This paper reports cluster-based cooperative spectrum sensing (CB-CSS) over Weibull, Nakagami, Rician, and Rayleigh fading channels in cognitive radio networks (CRNs). The performance assessment of centralized cooperative spectrum sensing (CCSS) and CB-CSS with different fusion rules over the Weibull fading channel is presented. The goal is to improve detection performance in a practical and efficient manner for CB-CSS. For this purpose, an expression for the optimal inter-fusion rule threshold (fusion rule between cluster head (CH) and fusion center (FC)) is proposed to minimize total error probability (TEP) over Weibull for CB-CSS. Simulations are conducted using analytical equations for missed detection and false alarm probability. The obtained expression for optimal inter-fusion rule threshold which minimizes TEP is verified by the results. The proposed model is validated with other fading channels such as Nakagami, Rician, and Rayleigh to show the superiority of the approach. It is observed that for a given range of detection threshold and for 4 × 6 cluster size (4 clusters and 6 CR users inside each cluster), the lowest total error probability is achieved over the Weibull fading channel. Minimum TEP over the Weibull fading channel for given network parameters is TEP = 1.656 × 10 − 6 when intra- and inter-fusion rules are l = 1 (OR fusion) and c = 3 . The least number of clusters and users essential to achieving target error bound over different fading channels are also determined.

Reinforcement learning for performance improvement in cooperative spectrum sensing

Cognitive radio (CR) can provide a promising solution to the spectrum scarcity issue for upcoming wireless communication technologies. Cooperative spectrum sensing (CSS) is generally adopted to improve spectrum utilization and minimize interference to primary users (PUs). The performance of CSS is significantly affected by imperfect reporting channels, and it is an easy target for Byzantine attackers. This paper studies CSS under imperfect reporting channels and Byzantine attacks. We have considered centralized CSS with a hard combining fusion rule. The binary symmetric channel (BSC) is used to model the imperfect reporting channels, and a centralized independent probabilistic small scale attack model is chosen to model Byzantine attackers. We first analyze the traditional CSS (T-CSS) under the imperfect reporting channel and the Byzantine attack. The performance of T-CSS is found to be greatly affected in the considered scenarios. We propose a reinforcement learning-based algorithm to detect cooperating secondary users (CSUs) experiencing weak reporting channels and Byzantine attackers. Generally, in literature, the detected malicious users (MUs) are removed to improve the performance. However, in case there are CSUs with weak reporting channels, the genuine CSUs may be detected as Byzantine attackers and hence removed. To overcome this issue, we propose a weighted sum-based CSS (WS-CSS) algorithm that can improve the CSS performance under weak reporting channels and attacks from Byzantine attackers. It is demonstrated using plots that the proposed WS-CSS algorithm significantly improves the CSS performance.

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximum-to-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

Multiantenna-Cognitive-Radio-Based Blind Spectrum Sensing under Correlated Signals and Unequal Signal and Noise Powers

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

In centralized cooperative spectrum sensing (CSS), cognitive radios (CRs) monitor the spectrum and send the related data to a fusion center (FC) via a control channel in order to more efficiently detect possible idle bands for opportunistic occupation. In centralized cooperative spectrum sensing (CSS), cognitive radios (CRs) monitor the spectrum and send the related data to a fusion center (FC) via a control channel in order to more efficiently detect possible idle bands for opportunistic occupation. In practice, such data must be quantized prior to transmission, which can lead to loss of the global spectrum sensing performance due to quantization errors. Additionally, wireless control channel impairments, such as the multipath fading, further contributes to performance degradation. In many researches, the control channel is considered error-free. Even when errors are considered, they usually affect the transmitted symbols independently, that is, the control channel is assumed to be memoryless. Error bursts come from the memory effect of the channel, and are typically found in wireless communications. In this paper we consider the effects of burst errors in the control channel as well as the problem of signal distortion caused by three methods of quantization in centralized CSS with sample fusion. The Gerschgorin Radii and Centers Ratio (GRCR) detector is used as the test statistic for spectrum sensing. Our findings show that: i) the GRCR is robust in the scenarios taken into consideration, ii) the uniform quantization may be preferred in some cases when the control channel is considered perfect, iii) the nonuniform quantization attains better performance under errors in the control channel, and iv) the effect of memory in the control channel may produce, in some situations, performance gains with respect to the memoryless channel, when both have the same average bit error rate.

One-to-Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

The centralized cooperative spectrum sensing (CSS) allows unlicensed users to share their local sensing observations with the fusion center (FC) for sensing the licensed user spectrum. Although collaboration leads to better sensing, malicious user (MU) participation in CSS results in performance degradation. The proposed technique is based on Kullback Leibler Divergence (KLD) algorithm for mitigating the MUs attack in CSS. The secondary users (SUs) inform FC about the primary user (PU) spectrum availability by sending received energy statistics. Unlike the previous KLD algorithm where the individual SU sensing information is utilized for measuring the KLD, in this work MUs are identified and separated based on the individual SU decision and the average sensing statistics received from all other users. The proposed KLD assigns lower weights to the sensing information of MUs, while the normal SUs information receives higher weights. The proposed method has been tested in the presence of always yes, always no, opposite, and random opposite MUs. Simulations confirm that the proposed KLD scheme has surpassed the existing soft combination schemes in estimating the PU activity.

Enhancement in Gain for Centralized Cooperative Spectrum Sensing in Cognitive Radio Network using Improved Local Sensing

Enhancement in Gain for Centralized Cooperative Spectrum Sensing in Cognitive Radio Network using Improved Local Sensing

Performance Analysis of Centralized Cooperative Spectrum Sensing Technique for Cognitive Radio Networks

Performance Analysis of Centralized Cooperative Spectrum Sensing Technique for Cognitive Radio Networks