ProMAC: A proactive model predictive control based MAC protocol for cognitive radio vehicular networks

Abstract

Cognitive Radio (CR) is a recent network paradigm that allows Secondary Users (SUs), such as, wireless devices/users, to intelligently access portions of the radio spectrum not allocated to it, without interfering with the transmission of licensed users (Primary Users (PUs)) who are allocated certain dedicated portions of the radio spectrum. This paradigm in radio communication has been successfully used in vehicular networks wherein communication can be established within vehicles (vehicle-to-vehicle) or vehicles to static stations (vehicle-to-infrastructure) without allocating dedicated frequencies. However, the challenge in CR design lies in building intelligence that helps in efficiently sensing and transmitting data through available radio spectrum channels. This paper proposes a Model Predictive Control (MPC) based Proactive Medium Access Control protocol (ProMAC) for the SUs in a CR network. To the best of our knowledge this is the first proactive MAC reported in the literature for CR. Employing ProMAC in a architecture where the number of SUs and PUs were constant, we achieved 20%,13.5% and 12% improvement in channel utilization, backoff rate and sensing delay respectively as compared to the recently proposed PO-MAC protocol, which is so far the best reported in the literature. In an architecture where the numbers of SUs varied with time, ProMAC achieved 21% and 13.17% improvement in channel utilization and backoff rate, respectively, as compared to PO-MAC. The proposed protocol is based on a self-learning engine that can evolve and improve its prediction accuracy even after deployment on field.