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Optimizing DF Cognitive Radio Networks With Full-Duplex-Enabled Energy Access Points

机译:使用启用全双工的能量访问点优化DF认知无线电网络

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With the recent advances in radio frequency (RF) energy harvesting (EH) technologies, wireless powered cooperative cognitive radio network (CCRN) has drawn an upsurge of interest for improving the spectrum utilization with incentive to motivate joint information and energy cooperation between the primary and secondary systems. Dedicated energy beamforming is aimed at remedying the low efficiency of wireless power transfer, which nevertheless arouses out-of-band EH phases and thus low cooperation efficiency. To address this issue, in this paper, we consider a novel CCRN aided by full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to wireless charge the secondary transmitter while concurrently receiving primary transmitter’s signal in the first transmission phase, and to perform decode-and-forward relaying in the second transmission phase. We investigate a weighted sum-rate maximization problem subject to transmitting power constraints as well as a total cost constraint using successive convex approximation techniques. A zero-forcing-based suboptimal scheme that requires only local channel state information for the EAPs to obtain their optimum receiving beamforming is also derived. Various tradeoffs between the weighted sum-rate and other system parameters are provided in numerical results to corroborate the effectiveness of the proposed solutions against the benchmark ones.
机译:随着射频(RF)能量收集(EH)技术的最新发展,无线协作式认知无线电网络(CCRN)引起了人们对提高频谱利用率的兴趣激增,并激励了初级和初级用户之间的联合信息和能源合作。二次系统。专用的能量波束成形旨在纠正无线电力传输的低效率,但是这仍然会引起带外EH阶段,从而降低协作效率。为了解决这个问题,在本文中,我们考虑了一种新型的CCRN,它由支持全双工(FD)的能量访问点(EAP)辅助,可以协作为辅助发射机无线充电,同时在第一传输阶段同时接收主发射机的信号,并在第二传输阶段执行解码转发中继。我们使用连续凸逼近技术研究受发射功率约束以及总成本约束的加权求和率最大化问题。还推导了一个基于零强迫的次优方案,该方案仅需要本地信道状态信息供EAP获得其最佳接收波束成形。在数值结果中提供了加权总和率与其他系统参数之间的各种折衷,以证实所提出的解决方案相对于基准解决方案的有效性。

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