混合智能反射面辅助的通信感知一体化:高能效波束成形设计

Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication:Energy Efficient Beamforming Design

能量效率(EE)是5G+/6G无线通信的重要设计指标,而智能反射面(RIS)被普遍认为是改善EE的潜在手段。不同于被动RIS,混合RIS由有源和无源元件组成,对来波移相的同时可放大信号强度,能够有效克服被动RIS引起的“乘性衰落”效应。鉴于此,该文提出一种混合RIS辅助通信感知一体化(ISAC)的下行链路传输系统。为探究数据传输速率与能耗之间的内在关联,该文以RIS辅助ISAC网络能量效率最大化为目标,在满足基站(BS)发射功率、波束图增益以及混合RIS功率和幅值约束的条件下,联合优化基站端的波束赋形和混合RIS的相移。为解决该复杂的分数规划问题,提出基于交替优化(AO)的算法来求解。为克服AO算法中引入辅助变量造成算法复杂度高的难题,利用耦合优化变量的关联,提出一种基于级联深度学习网络的求解算法。仿真结果表明,提出的混合RIS辅助ISAC方案在和速率、能效方面皆优于现有方案,且算法收敛速度快。

Energy Efficiency(EE)is an important design metric for 5G+/6G wireless communications,and Reconfigurable Intelligent Surface(RIS)is widely recognized as a potential means to improve EE.Unlike passive RIS,hybrid RIS consists of both active and passive components,which can amplify the signal strength while phase-shifting the incoming wave,and can effectively overcome the“multiplicative fading”effect caused by fully passive RIS.In view of this,a hybrid RIS-assisted Integrated Sensing and Communication(ISAC)downlink transmission system is proposed in this paper.In order to investigate the intrinsic correlation between data transmission capacity and energy consumption,the paper jointly optimizes the beamforming and phase-shifting of hybrid RIS at the Base Station(BS)under the constraints of BS transmit power,beampattern gain,and hybrid RIS power and amplitude with the goal of maximizing the global EE in a multiuser network.To solve this complex fractional programming problem,an algorithm based on Alternating Optimization(AO)is proposed to solve it.To overcome the problem of high algorithm complexity caused by the introduction of auxiliary variables in the AO algorithm,a solution algorithm based on a cascaded deep learning network is proposed using the association of coupled optimization variables.Simulation results show that the proposed hybrid RIS-assisted ISAC scheme outperforms existing schemes in terms of sum rate and EE,and the algorithm converges quickly.