Throughput-Efficient Channel Allocation in Multi-Channel Cognitive Vehicular Networks

Recent studies show that the Dedicated Short Range Communication (DSRC) band allocated to vehicular networks is insufficient to carry the wireless traffic load generated by emerging applications for vehicular systems. A promising bandwidth expansion possibility presents itself through the release of large TV band spectra by FCC for cognitive access. One of the primary challenges of the so-called TV White Space (TVWS) access in vehicular networks is the design of efficient channel allocation mechanisms in face of high vehicular mobility and spatial-temporal variations of TVWS. In this paper, we address the channel allocation problem for multi-channel cognitive vehicular networks with the objective of system-wide throughput maximization. We show that the problem is a NP-hard combinatorial optimization problem, to which we present two solution approaches. We first propose a probabilistic polynomial-time (1 - 1/e)-approximation algorithm based on linear programming. Next, we prove that our objective function can be written as a submodular set function, based on which we develop a deterministic polynomial-time constant-factor approximation algorithm with a more favorable time complexity. Finally, we show the efficacy of our algorithms through numerical examples.