Random sensing order in cognitive radio systems: Performance evaluation and optimization

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while assuring predetermined quality of service levels for the primary users. In this abstract, modeling, performance evaluation, and optimization of a distributed secondary network with random sensing order policy are studied. Specifically, the secondary users create a random order of the available channels upon primary users return, and then find an optimal transmission opportunity in a distributed manner. After modeling the behavior of the SUs by a Markov chain, the average throughputs of the secondary users and interference level among the secondary and primary users are evaluated. Then, a maximization of the secondary network performance in terms of throughput while keeping under control the average interference is proposed. A simple and practical adaptive algorithm is developed to optimize the network. Interestingly, the proposed algorithm follows the variations of the wireless channels in non-stationary conditions and outperforms even static brute force optimization, while demanding few computations. Finally, numerical results are provided to demonstrate the efficiencies of the proposed schemes. It is shown that fully distributed algorithms can achieve substantial performance improvements in cognitive radio networks without the need of centralized management or message passing among the users.