Weighted Cooperative Spectrum Sensing for Cognitive Vehicular Networks

Abstract

With the rapid development of intelligent transportation systems, vehicular devices are getting connected with each other. However, this leads to the problem of spectrum scarcity. Dynamic spectrum access (DSA)/cognitive radio (CR) has emerged as an effective solution to solve the problem of inefficient spectrum utilization. Spectrum sensing is the key in DSA/CR system. In cognitive vehicular networks (CVNs), spectrum sensing becomes more complex and challenging and that often leads to a loss in performance detection. Due to the effect of channel fading/shadowing and due to secondary user (SU) mobility, individual SUs may not be able to detect the existence of primary user (PU). In this paper, we propose a weighted cooperative spectrum sensing (weighted-CSS) framework for accurate detection of PU in CVNs. The weights are calculated from the probability of PU being inside the SU's sensing range and SU being outside the PU's protection range (inside probability). The calculated weight for SU indicates the reliability in the signal received by SU. The framework contains two stages. In the first stage, inside probability is calculated at each SU and the inside probability and the energy signal received from PU are sent to a base station (BS). In the second stage, BS assigns a weight to each SU based on the inside probability and makes a decision by combining the information received from SUs. Numerical results indicate that, on an average, the proposed framework performs ≈15% better than the conventional local spectrum sensing.