IRS辅助认知无线携能通信网络的发射功率最小化算法

Transmit Power Minimization Algorithms for IRS-Assisted Cognitive Simultaneous Wireless Information and Power Transfer Networks

智能反射平面(IRS)和认知无线携能通信技术被视为是提高能量效率和频谱利用率的潜在关键技术。文中研究了基于非线性能量采集模型的IRS辅助认知无线携能通信网络,其中次用户发射机同时给多个次用户接收机发送信息和能量,每个次用户接收机采用功率分割方式实现信息解码与能量采集,目的是通过联合优化次用户发射机的波束成形矢量、次用户接收机的功率分割系数以及IRS相移使次用户发射机的发射功率最小化。为了保证次用户发射机的信息与能量传输效率并限制次用户发射机对主用户接收机的同频干扰,考虑次用户接收机具有最小信干噪比约束、最小能量采集约束与功率分割系数约束,次用户发射机对主用户接收机有最大干扰功率值约束,以及IRS具有反射相移约束。所构建的问题属于非凸的二次约束二次规划问题,并且优化变量之间高度耦合,难以求解。文中提出一种基于半正定松弛法和连续秩一约束松弛法的交替优化算法进行高效求解。为了降低复杂度,进一步提出一种基于IRS分组的低复杂度优化算法。仿真结果表明,与几种基准算法相比,所提算法能够有效降低次用户发射机的发射功率。

Intelligent reflecting surface(IRS)and cognitive simultaneous wireless information and power transfer(SWIPT)are regarded as potential key technologies to improve energy efficiency and spectrum utilization of wireless communication systems.This paper studied the IRS-aided cognitive SWIPT network based on a nonlinear energy harvesting model.In the network,a secondary transmitter simultaneously transmits information and energy to multiple secondary receivers,and each secondary receiver adopts the power splitting scheme to realize information decoding and energy harvesting.The aim is to minimize the transmit power of the secondary user transmitter by jointly optimizing the beamforming vector of the secondary transmitter,the power splitting coefficients of the secondary receivers,and the phase shifts of the IRS.In order to guarantee the information and energy transmission efficiency of the secondary users and limit the co-channel interference from the secondary users to the primary users,it is considered that the secondary receivers have the constraints of the minimum received signal-to-interference noise ratio,the minimum energy harvesting amount,and the values of power splitting coefficients,the secondary transmitter has the constraints of the maximum interference power values to the primary users,and the IRS has the constraints on its reflection phase shifts.The considered optimization problem is a non-convex quadratically constrained quadratic program problem with highly coupled optimization variables,which is difficult to solve.An alternating optimization algorithm based on the semidefinite relaxation and sequential rank one constraint relaxation techniques was proposed to solve the problem efficiently.In order to reduce the computation complexity,a low-complexity optimization algorithm based on IRS element grouping was further proposed.Simulation results show that compared to several benchmark algorithms,the proposed algorithms can effectively reduce the transmit power of the secondary transmitter.