论文标题
IRS辅助MISO WPCN中的吞吐量最大化的联合波束形成和功率控制
Joint Beamforming and Power Control for Throughput Maximization in IRS-assisted MISO WPCNs
论文作者
论文摘要
智能反射表面（IRS）是一项新兴技术，可增强无线电动通信网络（WPCN）的能量和光谱效率。在本文中，我们研究了IRS辅助的多源多输入单输出（MISO）WPCN，其中单人体无线设备（WDS）收获下链路中的无线能量（DL）中的无线能量，并在上行链路（UL）中同时传输其信息，以与配有多个Antennas配备的常见混合接入点（HAP）。我们的目标是最大化所有能源收获用户的加权总和（WSR）。为了充分利用由HAP和IRS提供的波束形成增益，我们共同优化了DL和UL传输中IRS的HAP和反射系数（被动光束）的主动光束，以及WDS的发射功率减轻了HAP的互相干扰。为了解决具有挑战性的优化问题，我们首先考虑固定被动式波束形成，并将其剩余的关节主动束成式和用户传输功率控制问题转换为等效加权最小均值误差（WMMSE）问题，在此我们使用有效的块配位下降（BCD）方法来解决它。然后，我们使用半芬特松弛（SDR）方法来固定主动波束形成和用户发射功率，并在DL和UL中优化IRS的无源波束成形系数。因此，我们应用了块结构化优化（BSO）方法，以交替更新两组变量。数值结果表明，所提出的关节优化比其他代表性的基准方法可实现显着的性能增长，并有效地改善了多源味owpcn中的吞吐量。
Intelligent reflecting surface (IRS) is an emerging technology to enhance the energy- and spectrum-efficiency of wireless powered communication networks (WPCNs). In this paper, we investigate an IRS-assisted multiuser multiple-input single-output (MISO) WPCN, where the single-antenna wireless devices (WDs) harvest wireless energy in the downlink (DL) and transmit their information simultaneously in the uplink (UL) to a common hybrid access point (HAP) equipped with multiple antennas. Our goal is to maximize the weighted sum rate (WSR) of all the energy-harvesting users. To make full use of the beamforming gain provided by both the HAP and the IRS, we jointly optimize the active beamforming of the HAP and the reflecting coefficients (passive beamforming) of the IRS in both DL and UL transmissions, as well as the transmit power of the WDs to mitigate the inter-user interference at the HAP. To tackle the challenging optimization problem, we first consider fixing the passive beamforming, and converting the remaining joint active beamforming and user transmit power control problem into an equivalent weighted minimum mean square error (WMMSE) problem, where we solve it using an efficient block-coordinate descent (BCD) method. Then, we fix the active beamforming and user transmit power, and optimize the passive beamforming coefficients of the IRS in both the DL and UL using a semidefinite relaxation (SDR) method. Accordingly, we apply a block-structured optimization (BSO) method to update the two sets of variables alternately. Numerical results show that the proposed joint optimization achieves significant performance gain over other representative benchmark methods and effectively improves the throughput performance in multiuser MISO WPCNs.
