Besides, the requirement for accurate exchanged information will cost wireless devices unacceptable long time to collect enough information when taking into consideration the possible collisions during communications. Extensive simulation results demonstrate the prefer-ence of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters. 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 clus-ter. Different from origi-nal 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. 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. In this paper, we propose a distributed clustering algo-rithm based on soft-constraint affinity propagation mes-sage passing model (DCSCAP). The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. Absence of network infrastructure and hetero-geneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordi-nation.
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