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Multicast throughput optimization and fair spectrum sharing in cognitive radio networks

机译:认知无线电网络中的组播吞吐量优化与公平频谱共享

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By enabling opportunistic secondary users' (SUs) usage of licensed spectrum, cognitive radio (CR) technology notably improves spectrum utilization. However, the fundamental multicast throughput optimization problem in cognitive radio networks (CRNs) is still under-explored. Considering spectrum availability and sharing fairness, in this paper, we propose a cross-layer approach to maximize the multicast throughput in multi-hop CRNs. We introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum and allocate the collected bands among SUs. The SSP also guides the transmissions of multicast CR sessions w.r.t. their contention and spectrum sharing fairness. Leveraging the proposed palmier structure for the multicast session and the multi-radio multi-band multicast (M3) conflict graph, we mathematically characterize the multicast flow routing and link scheduling, respectively. Based on the proportional fairness model, we formulate the multicast maximization problem under multiple cross-layer constraints in CRNs, and provide near-optimal solutions. Through simulations, we show that the performance of the proposed scheme is much better than that of schemes without cross-layer consideration.
机译:通过实现机会主义的二级用户(SUS)使用许可频谱,认知无线电(CR)技术显着提高了频谱利用率。然而,仍然探索认知无线电网络(CRNS)中的基本组播吞吐量优化问题。考虑到频谱可用性和共享公平性,在本文中,我们提出了一种跨层方法来最大化多跳CRN中的多播吞吐量。我们介绍一个新的服务提供商,称为辅助服务提供商(SSP),以收集可用频谱并分配SUS之间的收集的频带。 SSP还指导多播CR会话W.R.T的传输。他们的争论和频谱共享公平。利用所提出的Palmier结构,为多播会话和多无线电多频段多播(M3)冲突图,我们分别计算多播流路由和链路调度。基于比例公平模型,我们在CRNS中的多个跨层约束下制定多播最大化问题,并提供近最佳解决方案。通过模拟,我们表明所提出的方案的性能远比没有交叉层考虑的方案的表现。

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