Cooperative Spectrum Sensing based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. Most of such techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far in this field are based on asymptotical assumptions, due to the difficulties in characterizing the exact distribution of the eigenvalues ratio. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then straightforwardly applied to calculate the decision threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial performance improvement compared to the other eigenvalue-based algorithms.Comment: 7 pages, 2 figures, submitted to IEEE Communications Letter

Cooperative wideband spectrum sensing with multi-bit hard decision in cognitive radio

Cognitive radio offers an increasingly attractive solution to overcome the underutilization problem. A sensor network based cooperative wideband spectrum sensing is proposed in this paper. The purpose of the sensor network is to determine the frequencies of the sources and reduced the total sensing time using a multi-resolution sensing technique. The final result is computed by data fusion of multi-bit decisions made by each cooperating secondary user. Simulation results show improved performance in energy efficiency

A Multi-layer Routing Protocol for Mobility Management in Wireless Mesh Networks

AbstractIn the recent trends, Wireless Mesh networks are proven to be one of the emerging fields in the wireless networks. WMNs comprises of Gateways (GWs), Mesh Clients (MCs) and Mesh Routers (MRs). However, it is challenging job to provide seamless connectivity when MC moves around the network. The recent advances in the field of wireless technology created a chance to overwhelmed the disadvantages of wired and wireless networks. The mobility management in the WMNs motivated the researchers to concentrate. In this paper, we are proposing a model called as multi-layer routing protocol for WMNs. This protocol works with the data link layer and network layer for data frame transmission. The proposed algorithm is implemented with intra domain for experimental evaluation. The experimental results show the effectiveness of the routing protocol

Hard Combination Data Fusion for Cooperative Spectrum Sensing in Cognitive Radio

This paper presents a study of hard combination data fusion for cooperative spectrum sensing in Cognitive Radio (CR). We evaluated the performance of cooperative spectrum sensing with the hard combination OR, AND and MAJORITY rules. Energy detection technique is used to sense the presence of primary user (PU) signal. Simulation result shows that cooperative spectrum sensing with OR rule is the best among hard combination data fusion in Cognitive Radio and gives the better performance than AND and MAJORITY rules.DOI:http://dx.doi.org/10.11591/ijece.v2i6.181

Adaptive Techniques to Detect White Spaces Using Spectrum Sensing In Cognitive Radio

Spectrum sensing is one of the key technologies to realize dynamic spectrum access in cognitive radio systems. Cognitive radios have been proposed as a possible solution to improve spectrum utilization by enabling opportunistic spectrum sharing. The main requirement for allowing CR�s to use proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. Spectrum sensing allows cognitive users to autonomously identify unused portions of the radio spectrum. In this work, energy detection technique is considered for spectrum sensing and uses the cost-function that depends upon a single parameter which gives the aggregate information about the present or absent of licensed users. The process of threshold selection for energy detection is addressed by the constant false alarm method and selection is carried out considering present conditions of noise levels. In this paper, simulation results shown that if we dynamically adjust the detection threshold based on noise level present during the detection process. The detection of white spaces will be higher at lower sampling time as compared with probability of detection and false alarm rate