Statistical CSI-Based Transmission Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Hardware Impairments

We consider a reconfigurable intelligent surface (RIS)-aided massive multi-user multiple-input multiple-output (MIMO) communication system with transceiver hardware impairments (HWIs) and RIS phase noise. Different from the existing contributions, the phase shifts of the RIS are designed based on the long-term angle information. Firstly, an approximate analytical expression of the uplink achievable rate is derived. Then, we use genetic algorithm (GA) to maximize the sum rate and the minimum date rate. Finally, we show that it is crucial to take HWIs into account when designing the phase shift of RIS.

[1]  Jiangzhou Wang,et al.  Tradeoff Caching Strategy of the Outage Probability and Fronthaul Usage in a Cloud-RAN , 2016, IEEE Transactions on Vehicular Technology.

[2]  Lajos Hanzo,et al.  Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces , 2019, IEEE Transactions on Wireless Communications.

[3]  Kezhi Wang,et al.  Power Scaling Law Analysis and Phase Shift Optimization of RIS-Aided Massive MIMO Systems With Statistical CSI , 2020, IEEE Transactions on Communications.

[4]  Rui Wang,et al.  Achievable Rate Analyses and Phase Shift Optimizations on Intelligent Reflecting Surface with Hardware Impairments , 2020, ArXiv.

[5]  Rui Wang,et al.  Energy Efficiency Analysis of Intelligent Reflecting Surface System with Hardware Impairments , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[6]  Mohamed-Slim Alouini,et al.  Wireless Communications Through Reconfigurable Intelligent Surfaces , 2019, IEEE Access.

[7]  Kezhi Wang,et al.  Statistical CSI-Based Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Direct Links , 2020, IEEE Wireless Communications Letters.

[8]  Michail Matthaiou,et al.  Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means , 2014, IEEE Journal of Selected Topics in Signal Processing.

[9]  Alexandros-Apostolos A. Boulogeorgos,et al.  How Much do Hardware Imperfections Affect the Performance of Reconfigurable Intelligent Surface-Assisted Systems? , 2020, IEEE Open Journal of the Communications Society.

[10]  Derrick Wing Kwan Ng,et al.  Beamforming Optimization for IRS-Aided Communications With Transceiver Hardware Impairments , 2020, IEEE Transactions on Communications.

[11]  Symeon Chatzinotas,et al.  Intelligent Reflecting Surface-assisted MU-MISO Systems with Imperfect Hardware: Channel Estimation, Beamforming Design , 2021, ArXiv.

[12]  Davide Dardari,et al.  Intelligent Reflecting Surfaces: Sum-Rate Optimization Based on Statistical Position Information , 2021, IEEE Transactions on Communications.

[13]  Mohamed-Slim Alouini,et al.  Smart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come , 2019, ArXiv.

[14]  Kezhi Wang,et al.  Uplink Achievable Rate of Intelligent Reflecting Surface-Aided Millimeter-Wave Communications with Low-Resolution ADC and Phase Noise , 2020 .