MU-MIMO Based Cognitive Radio in Internet of Vehicles (IoV) for Enhanced Spectrum Sensing Accuracy and Sum Rate

Abstract

Vehicular ad-hoc networks (VANETs) provide the basic infrastructure for intelligent transportation systems (ITS), as it allows vehicles to access the Internet and to communicate intra-vehicle, inter-vehicle and vehicle to the roadside base station. The Internet of Vehicles (IoV) is an evolution of VANETs following the IoT paradigm. Nowadays, the spectrum scarcity is a big issue for the IoV networks due to the increased demand for connecting more vehicles. The cognitive radio (CR) enabled IoV networks can address this issue. In this paper, we propose a multi-user multiple-input and multiple-output (MU-MIMO) antennas aided cluster based cooperative spectrum sensing (CB-CSS) scheme for CR enabled IoV networks. In this proposed scheme, each CR embedded vehicles (CRV) sends sensing data to the cluster head (CH) which makes a cluster decision by using the soft data fusion rule like the equal gain combining (EGC) fusion rule and the maximal ratio combining (MRC) fusion rule; whereas the fusion centre (FC) makes a final global decision by using the K-out-of-N rule to identify the presence of the PU signal. Simulation results show that the proposed MU-MIMO antennas aided CB-CSS scheme achieves a better sensing gain, enhanced the sum rate and lower global error probability when compared to both the conventional single-input and single-output (SISO) antenna based cooperative spectrum sensing (CSS) and non-cooperative spectrum sensing (NCSS) schemes. In addition, the proposed scheme achieves a lower traffic overhead when compared to the MU-MIMO based CSS scheme without the cluster.