A linear-time algorithm for optimal multi-channel access in Cognitive Radio Networks

Abstract

Cognitive Radio Networks (CRNs) aim to maximize the utilization of existing wireless channels by allowing secondary users (SUs) to transmit when licensed primary users (PUs) are not using the same channels. An SU monitors the CRN channels, sensing PU presence to avoid interference and estimating the link quality before transmitting. It stops when one or more available channels with satisfactory link quality are found. Algorithms for making the optimal decision regarding when to stop exploring the channels and start transmitting are expensive in terms of time and space, which are both scarce in hardware-constrained SUs, such as mobile devices. In this paper, we propose a low-complexity algorithm, which utilizes link quality and PU-activity statistics of the CRN channels to pre-compute a set of decision thresholds that will aid the channel exploration phase in maximizing SU-throughput. Our algorithm takes quadratic time and space for offline computations and linear time and space for online processing, which makes it very suitable for space and energy constrained mobile SUs. Our extensive simulation study and analytical model matching the simulation results demonstrate our solution's validity by showing the closeness of throughput and delay performances with the optimum solution as well as solutions by the well-known backward induction method, which often runs in exponential time for offline computations.