Distributed random sensing order analysis and optimization in cognitive radio systems

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 paper, modeling, performance analysis, 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 and then find a transmission opportunity in a distributed manner. By a Markov chain analysis, the average throughputs of the secondary users and average interference level between the secondary and primary users are evaluated. Then, a maximization of the performance of the secondary network in terms of throughput while keeping under control the average interference is proposed. A simple and practical adaptive algorithm is established to optimize the network. Finally, numerical results are provided to validate the analytical derivations and demonstrate the performance of the proposed schemes. It is shown that distributed algorithms can achieve substantial performance improvements in cognitive radio networks without the need of centralized operations or management.