This paper considers an intelligent reflecting surface(IRS)-aided wireless powered communication network (WPCN), where devices first harvest energy from a power station (PS) in the downlink (DL) and then transmit information using non-orthogonal multiple access (NOMA) to a data sink in the uplink (UL). However, most existing works on WPCNs adopted the simplified linear energy-harvesting model and also cannot guarantee strict user quality-of-service requirements. To address these issues, we aim to minimize the total transmit energy consumption at the PS by jointly optimizing the resource allocation and IRS phase shifts over time, subject to the minimum throughput requirements of all devices. The formulated problem is decomposed into two subproblems, and solved iteratively in an alternative manner by employing difference of convex functions programming, successive convex approximation, and penalty-based algorithm. Numerical results demonstrate the significant performance gains achieved by the proposed algorithm over benchmark schemes and reveal the benefits of integrating IRS into WPCNs. In particular, employing different IRS phase shifts over UL and DL outperforms the case with static IRS beamforming.
翻译:本文考虑了一种智能反映表面(IRS)辅助无线动力通信网络(WPCN),在这种网络中,装置首先从下行的电线下行的电站(PS)获得能源,然后用非横向多重存取(NOMA)向上行(UL)的数据槽传递信息。然而,关于WPCN的大多数现有工作采用了简化线性能源收获模型,也无法保证严格的用户服务质量要求。为了解决这些问题,我们的目标是通过联合优化资源分配和IRS阶段的逐步转移,尽量减少PS的能源消耗总量,但须符合所有装置的最低吞吐要求。所提出的问题被分解成两个子问题,并以替代的方式通过使用对等函数编程的不同、对等相近和基于罚款的算法,以迭代方式解决。数字结果表明拟议的算法在基准计划方面所取得的重大绩效收益,并揭示了将IRS纳入WPCN的效益。特别是,使用不同的IRS逐步在UL和DL上移动,使案件与静态IRS形成。