Phase-Shift and Transmit Power Optimization for RIS-Aided Massive MIMO SWIPT IoT Networks

• Published in: IEEE transactions on communications Vol. 73; no. 1; pp. 631 - 647
• Main Authors: Mohammadi, Mohammadali, Ngo, Hien Quoc, Matthaiou, Michail
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Beamforming; Contamination; Devices; Energy limitation; Internet of Things; Massive MIMO; Massive multiple-input multiple-output (MIMO); MIMO communication; Optimization; Power transfer; Precoding; reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Resource allocation; Simulation; Simultaneous wireless information and power transfer; simultaneous wireless information and power transfer (SWIPT); Wireless communication; Wireless networks
• DOI: 10.1109/TCOMM.2024.3432455
• ISSN: 0090-6778

We investigate reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) Internet of Things (IoT) networks, where energy-limited IoT devices are overlaid with cellular information users (IUs). IoT devices are wirelessly powered by a RIS-assisted massive multiple-input multiple-output (MIMO) base station (BS), which is simultaneously serving a group of IUs. By leveraging a two-timescale transmission scheme, precoding at the BS is developed based on the instantaneous channel state information (CSI), while the passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. We derive closed-form expressions for the achievable spectral efficiency of the IUs and average harvested energy at the IoT devices, taking the channel estimation errors and pilot contamination into account. Then, a non-convex max-min fairness optimization problem is formulated subject to the power budget at the BS and individual quality of service requirements of IUs, where the transmit power levels at the BS and passive RIS reflection coefficients are jointly optimized. Our simulation results show that the average harvested energy at the IoT devices can be improved by 132% with the proposed resource allocation algorithm. Interestingly, IoT devices benefit from the pilot contamination, leading to a potential doubling of the harvested energy in certain network configurations.