Abstract
The observed unbalance of spectrum utilization combined with the proliferation of wireless devices and the growth of online social networks call for a joint study of cognitive radios (CRs) and social networking towards improving the operation of both the underlying CR-capable communications infrastructures and of the corresponding dynamic networks. In this work, we address such issue and focus on the twofold direction of resource allocation in CRs and social feature exploitation with the aim of providing a holistic cross-layer optimization framework that jointly considers information from the social layer as well as the traditional network protocol layers. We combine Markov random field (MRF) cross-layer decisions with back pressure (BP) features for the allocation of resources, in order to improve the operation of CRs that act as social network substrate. Espousing the recent advances in the field of dynamic spectrum allocation, the proposed BP-enhanced MRF (BPeMRF) network optimization serves the purpose of improving in a low complexity manner the capacity of agile infrastructures, which are required to adapt fast according to the demands of the volatile CR environment and the overlaying dynamic online social networks. In addition, within the proposed socially enhanced BPeMRF (SeBPeMRF) approach, we leverage emerging social information to further enhance performance. The obtained results exhibit the efficacy of BPeMRF/SeBPeMRF regarding the above objectives and demonstrate significant promise for further improving the corresponding infrastructures and services.
Highlights
Various studies [1,2] have proved the significant unbalance of spectrum assignment and utilization generated by the traditional static spectrum allocation practices worldwide
It seems that cognitive radios (CRs) and social networking are becoming more integrated in the future wireless Internet, in various capacities, and they seem to converge in the sense that CRs may constitute one of the major infrastructures to carry online social network traffic and meet the corresponding demands imposed by the latter
We introduce scenario 2 for the SeBPeMRF algorithm aiming to illustrate the observation in Remark 2, based on which when ρc remains constant while wcmax and wcmin increase for a commodity c, c is likely to gain with respect to performance
Summary
Various studies [1,2] have proved the significant unbalance of spectrum assignment and utilization generated by the traditional static spectrum allocation practices worldwide. We focus on leveraging MRF structure and Gibbs sampling combined with back pressure features such that throughput optimal joint scheduling and routing is efficiently achieved along with channel allocation and power control in the secondary network.
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