Game Theoretic Dynamic Spectrum Access in Cloud-Based Cognitive Radio Networks

Abstract

Radio Frequency (RF) resource allocation in a Cognitive Radio Network (CRN) is considerably constrained by its limited power, memory and computational capacity. With the emergence of cloud computing platforms, CRN has the potential to mitigate these constraints by leveraging the vast storage and computational capacity. In this paper, we proposed a game theoretic approach for resource allocation in cloud-base cognitive radio network. The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate dynamic spectrum access to secondary users. Furthermore, the active secondary users adapt their transmit power using game theoretic approach in distributed manner based on the network condition in terms of estimated average packet error rate while satisfying the Quality-of-Service (QoS) in terms of signal-to-interference-plus-noise ratio. To control greedy secondary users in distributed power control game, we introduce a manager through a Stackelberg power adaptation game. Simulation results are presented to demonstrate the performance of the proposed radio resource management algorithm.