Optimal Joint Power and Rate Adaptation for Awareness and Congestion Control in Vehicular Networks

Abstract

To support inter-vehicular applications, vehicles broadcast beacons with information about their state. Congestion may occur when the load on the channel due to beaconing can prevent the transmission of other types of messages. In this paper, we propose a distributed algorithm for optimal joint adaptation of transmit power and beaconing rate for congestion and awareness control. Our approach is based on a network utility formulation of the congestion control problem, which allows us to induce a desired fairness notion and set different priorities for vehicles. We formulate the general problem but, since it is not convex, we assume Rayleigh fading and derive an algorithm, called PRAIOS, that solves the optimization problem in a decentralized way with convergence guarantees. Our results, validated with realistic simulations in both static and dynamic scenarios and compared with other proposals, show that PRAIOS quickly converges to close to optimal allocations, while keeping the maximum beaconing load at the desired level. They also show that it can control the load when the fading is not Rayleigh. Applications can dynamically set their requirements as constraints, that are enforced by the algorithm while complying with the maximum load, which allows seamless integration of operational requirements into the control framework.