Performance Analysis of RIS-Aided Wireless-Powered Cell-Free IoT Networks With Imperfect Statistical CSI

Reconfigurable intelligent surface (RIS) has the potential to revolutionize wireless communications by dynamically controlling wireless channels to boost spectral efficiency (SE) and energy efficiency (EE), towards meeting the advanced specifications of Internet of Things (IoT) networks. In this context, we study the downlink harvested energy (HE), uplink SE and total EE of the RIS-aided cell-free massive multiple-input multiple-output (CF-mMIMO) system with wireless power transfer (WPT) technology. 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. Based on the unique characteristics of the channel fading model and the RIS deployment location, we put forward a novel RIS phase shift design scheme according to the line-of-sight (LoS) components of channels and verify its effectiveness. Furthermore, we derive the average HE and uplink SE in closed form with a two-layer decoding method (i.e., the maximal ratio combining (MRC) at APs is called first-layer decoding and the large-scale fading decoding (LSFD) at CPU is called second-layer decoding.) under the assumptions of both perfect and imperfect statistical channel state information (CSI). The results verify the derived closed-form expressions by Monte-Carlo simulations. Increasing the number of RIS elements further improves the uplink SE and total EE with the two-layer decoding. Since the statistical CSI is unknown in practical scenarios, we propose an acquisition method for the statistical CSI applicable to this system. Simulation results validate the efficiency of the proposed statistical CSI acquisition method. Furthermore, it is interesting to find that better statistical CSI estimation can be achieved with more coherent blocks of pilot.