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Joint channel selection and cluster-based routing scheme based on reinforcement learning for cognitive radio networks

机译：基于增强学习的认知无线电网络联合信道选择和基于集群的路由方案

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Cognitive radio network (CRN) has emerged as a promising solution to solve the problem of underutilization of licensed spectrum. It allows opportunistic access of unutilized spectrum (or white spaces) by unlicensed users (or secondary users, SUs) whilst minimizing interference to licensed users (or primary users, PUs). The dynamicity of channel availability has imposed additional challenges for routing in CRNs. Besides providing optimal routes to SUs for communication, one of the key requirements of routing in CRNs is to minimize interference to PUs. In this paper, we propose a joint channel selection and cluster-based routing scheme called SMART (SpectruM-Aware cluster-based RouTing) for CRNs. SMART enables SUs to form clusters in the network, and subsequently, it enables SU source node to search for a route to its destination node in the underlying clustered network. SMART applies an artificial intelligence approach called reinforcement learning in order to maximize network performance, such as SU-PU interference and packet delivery ratio. Simulation results show that SMART reduces significant interference to PUs without significance degradation of packet delivery ratio when compared to clustered scheme without cluster maintenance (i.e., SMART-NO-MNT) and non-clustered scheme (i.e., spectrum-aware AODV or SA-AODV).
机译：认知无线电网络（CRN）已经成为解决许可频谱利用不足的有前途的解决方案。它允许未经许可的用户（或次要用户，SU）机会性地利用未利用的频谱（或空白），同时最大程度地减少对许可用户（或主要用户，PU）的干扰。信道可用性的动态性给CRN中的路由带来了额外的挑战。除了提供通向SU的最佳路由进行通信外，CRN中路由的关键要求之一就是最大程度地减少对PU的干扰。在本文中，我们提出了一种针对CRN的联合通道选择和基于群集的路由方案，称为SMART（基于SpectruM-Aware群集的路由）。 SMART使SU可以在网络中形成集群，随后，它使SU源节点可以在其下层集群网络中搜索到其目标节点的路由。 SMART应用一种称为强化学习的人工智能方法，以最大限度地提高网络性能，例如SU-PU干扰和数据包传输率。仿真结果表明，与没有群集维护的群集方案（即SMART-NO-MNT）和非群集方案（即频谱感知的AODV或SA-AODV）相比，SMART可以显着降低对PU的干扰，而不会显着降低数据包的传输率。 ）。

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