  Developing an efficient spectrum access policy enables cognitive radios to
dramatically increase spectrum utilization while ensuring predetermined quality
of service levels for 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 available channels upon primary users return, and then find
optimal transmission and handoff opportunities in a distributed manner. By a
Markov chain analysis, the average throughputs of the secondary users and
average interference level among the secondary and primary users are
investigated. A maximization of the secondary network performance in terms of
the throughput while keeping under control the average interference is
proposed. It is shown that despite of traditional view, non-zero false alarm in
the channel sensing can increase channel utilization, especially in a dense
secondary network where the contention is too high. Then, two simple and
practical adaptive algorithms are established to optimize the network. The
second algorithm follows the variations of the wireless channels in
non-stationary conditions and outperforms even static brute force optimization,
while demanding few computations. The convergence of the distributed algorithms
are theoretically investigated based on the analytical performance indicators
established by the Markov chain analysis. Finally, numerical results validate
the analytical derivations and demonstrate the efficiency of the proposed
schemes. It is concluded that fully distributed sensing order algorithms can
lead to substantial performance improvements in cognitive radio networks
without the need of centralized management or message passing among the users.
