6,519 research outputs found
Message-passing algorithms for optimal utilization of cognitive radio networks
Cognitive Radio has been proposed as a key technology to significantly improve spectrum usage in wireless networks by enabling unlicensed users to access unused resource. We present new algorithms that are needed for the implementation of opportunistic scheduling policies that maximize the throughput utilization of resources by secondary users, under maximum interference constraints imposed by existing primary users. Our approach is based on the Belief Propagation (BP) algorithm, which is advantageous due to its simplicity and potential for distributed implementation. We examine convergence properties and evaluate the performance of the proposed BP algorithms via simulations and demonstrate that the results compare favorably with a benchmark greedy strategy
Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation
Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters
Analysis and Optimization of Random Sensing Order in Cognitive Radio Networks
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.Comment: 16 pages, 12 figures, 7 tables, accepted in Journal of Selected Areas
in Communications (J-SAC) CR series and will be published in Apr'1
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