Dymamic MAC Frame Configuration and PSO-Based Optimal Resource Allocation in Multi-channel Cognitive Radio Ad-Hoc Networks

Abstract

In cluster-based cognitive radio networks, secondary system uses available channels that are not used temporally by primary systems. In multi-channel operational environments, each available channel may have different wireless channel gain and primary activity so that achievable data rate to secondary users (SUs) and required sensing parameter value are channel dependent. SUs also have different energy saving requirements and data traffic demands. Therefore, based on measured channel conditions and user constraints, cluster head needs to decide which channel should be allocated to which SUs and to configure the optimum MAC frame structure for satisfying each SU’s service demands. Furthermore, optimization to provide proportional fairness among the cognitive secondary users in resource allocation in terms of energy consumption and data rate is very important. In this paper, a dynamic MAC frame configuration and optimal resource allocation scheme for multi-channel ad-hoc cognitive radio network is proposed. We formulate our dynamic resource allocation model as a constrained optimization problem with multi-objective functions using particle swarm optimization (PSO) algorithm. The proposed PSO scheme guarantees that the allocation captures the individual traffic and energy saving demands and maximizes the objectives functions simultaneously. Simulation results show the proposed scheme can successfully maximize the intended utility function and provide proportional fairness between SUs.