This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a tw...This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(62271068)the Beijing Natural Science Foundation(L222046).
文摘This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.
