Infrastructure based spectrum sensing scheme in VANET using reinforcement learning

Abstract

Spectrum sensing is one of the fundamental functionality performed by a cognitive radio to identify vacant radio spectrum for dynamic spectrum access (DSA). However, there are many challenges still existing before the benefits of DSA can be realized. The challenges include multipath fading, shadowing and hidden primary user (PU) problem. The challenges are more severe in vehicular communication due to unique characteristics such as dynamic topology caused by vehicle mobility. Furthermore, spectrum sensing is dependent on the activities of the PU traffic pattern which are not known in advance. In a typical cognitive radio network, the PU plays a passive role. Therefore, a sensing technique should account for traffic pattern of the PU autonomously. However, most of the proposed spectrum sensing schemes in vehicular communication assumes a static ON/OFF PU model which does not realistically model the PU traffic pattern. In this paper, we propose reinforcement learning (RL) to model the traffic pattern of the PU and use the model to predict channels likely to be free in future. The RL is implemented on road side unit (RSU) which send predicted vacant PU channels to vehicles on the road. Before the channels can be used, vehicles perform spectrum sensing. To account for multipath fading and shadowing, adaptive spectrum sensing is proposed. The results from spectrum sensing, sensing time and PU channel capacity are calculated into a scalar value and used as reward for RL at RSU. The RSU continuously update the reward for channels of interest using sensing history from passing vehicles as reward. Compared to history based schemes from literature, the RL technique proposed in this paper performs better.