Wireless-Powered RIS-Aided Cell-Free Massive MIMO With Hardware Impairments for URLLC

Reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) technology has tremendous potential to revolutionize wireless communications by dynamically adapting wireless channels to boost average rate and energy efficiency (EE) of Internet of Things (IoT) networks for meeting the specifications of ultra-reliable and low-latency communications (URLLC). In this paper, we study the downlink harvested energy (HE), uplink rate, and total EE of the wireless-powered RIS-aided CF-mMIMO communication system with hardware impairments under finite blocklength. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Specifically, based on the unique characteristics of the channel fading model and the RIS deployment location, we propose a novel RIS phase shift design according to the line-of-sight (LoS) components of channels. Furthermore, we derive the average HE and uplink rate in closed form with a two-layer decoding method. We also validate the effectiveness of the proposed RIS phase shift design and the derived closed-form expressions by Monte Carlo simulations. Moreover, it is interesting to find that local minimum mean squared error (L-MMSE) combining is recommended to meet the requirements of URLLC, including communication reliability and delay. More notably, the numerical results show that the RIS-aided system with impaired hardware exhibits even superior performance, compared to the system with ideal hardware and more APs but lacking the assistance of RISs.