Anti-Shadowing Resource Allocation for General Mobile Cognitive Radio Networks

Abstract

Resource allocation (RA) for mobile secondary users is considered one of the most important techniques for designing the next-generation cognitive radio network (CRN). In this paper, effective capacity (EC) is proposed to improve the RA performance for an underlay-based mobile CRN. By optimizing EC, an efficient resource allocation scheme is developed. First, we consider a moving secondary system, where the channel state information can be predicted by location-awareness techniques. According to the prediction result, we set protecting parameters for both the secondary and the primary performance targets to minimize the prediction error introduced by the decorrelated shadowing. Second, the computed sum average EC of the cognitive system is maximized. To solve the optimization RA problem, a low-complexity stepwise algorithm is proposed based on four procedures aimed at access, initialization, subchannel, and power. Moreover, the speeds of the primary users are taken into account, and a general system model is built. The corresponding resource allocation solutions can be induced easily through extending the originally proposed solution. Finally, simulation results are provided to confirm the EC-based algorithm. The proposed approach can not only improve the total secondary capacity but also achieve higher energy efficiency and spectrum efficiency for the mobile secondary system.