Resource Allocation Algorithm for Cognitive Radic Network with Heterogeneous User Traffic

摘要

In this paper we present a novel approach for resource allocation in cognitive radio network (CRN) with heterogeneous user traffic. In this approach we deploy some form of reinforcement learning, and make a short-term resource allocation based on the long-term traffic prediction. The corresponding resource affocation aigorithm derived in the paper is impiemented in cognitive 3rd Generation Partnership Project Long Term Evoiution (3GPP LTE) network. Performance anaiysis of the aigorithm has shown that the proposed approach for resource affocation achieves better performance than other schemes designed to deaf with the probiem of heterogeneous user applications.