Demand-Aware Multichannel Opportunistic Spectrum Access: A Local Interaction Game Approach With Reduced Information Exchange

We investigate the problem of distributed channel selection in cognitive radio networks (CRNs) by taking the demands of the users into account. In most existing channel selection approaches, it is assumed that cognitive radio (CR) users can transmit on only one channel, which leads to a problem that the users usually cannot obtain enough transmission rates to satisfy their demands. We propose a demand-aware multichannel selection strategy focusing on optimizing CR users´ satisfaction. In the proposed strategy, CR users can transmit on more than one channel simultaneously. Since there is no central controller and the users are autonomous, we propose a local interaction game to achieve global optimization for the network satisfaction problem distributedly. In existing local interaction game, the players need to exchange information with all the neighboring users, which results in heavy communication overhead. We design a new local interaction game, in which CR users only need to exchange information with part of the neighboring users instead of all neighboring users. As a result, the exchanged information is significantly reduced. Simulation results show that the proposed method achieves higher network satisfaction and better fairness than the existing single-channel selection approach.