Cross-layer Resource Allocation with heterogeneous QoS requirements in cognitive radio networks

Abstract

To deal with the problem that cognitive information and queuing information are not both taken into account when performing Resource Allocation (RA), we have proposed a cross-layer RA scheme in a multiuser cognitive system with Orthogonal Frequency Division Multiple Access (OFDMA). In this scheme, we assume that several crucial factors influence the RA decision, including subchannel occupancy state, subchannel link state and packets queuing state. The RA strategy is formulated as a convex optimization problem to maximize the whole system throughput with users' heterogeneous Quality of Service (QoS) requirements, considering the dynamic variations of available resources caused by the activities of licensed users. In order to solve the problem with low complexity, we present Joint Resource Allocation (JRA) algorithm involving two sub-algorithms, Minimal-Cost-Maximal-Flow Subchannel Allocation (MCMFSA) and Suboptimal Power Allocation (SPA). The innovations of the entire paper are the integrity of system model and the novelty of these algorithms, especially combining subchannel allocation with graphical theory. Simulation results indicate that the proposed scheme can achieve an excellent performance, with all QoS requirements satisfied.