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

Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

TL;DR

This work studies RIS-aided SWIPT in a massive MIMO IoT downlink, employing a two-timescale scheme where BS precoding adapts to instantaneous CSI while RIS phase shifts adapt to long-term statistics. It derives closed-form expressions for downlink spectral efficiency of information users and average harvested energy at energy users under channel estimation errors and pilot contamination, and proposes two precoding schemes, partial zero-forcing (PZF) and protective PZF (PPZF), to balance SE and EH. A joint RIS phase-shift design and BS power-control problem is formulated to maximize the minimum harvested energy among energy users, solved via a block-coordinate-descent algorithm with SCA and quadratic-transform-based fractional programming; a double-layer penalty approach enforces rank-one RIS configurations. Numerical results show substantial harvested-energy gains (up to 132% in some setups) and reveal that pilot contamination can even boost EH, with RIS size enabling reductions in the required BS antenna count, highlighting practical energy efficiency benefits for RIS-enabled SWIPT systems.

Abstract

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.

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

TL;DR

This work studies RIS-aided SWIPT in a massive MIMO IoT downlink, employing a two-timescale scheme where BS precoding adapts to instantaneous CSI while RIS phase shifts adapt to long-term statistics. It derives closed-form expressions for downlink spectral efficiency of information users and average harvested energy at energy users under channel estimation errors and pilot contamination, and proposes two precoding schemes, partial zero-forcing (PZF) and protective PZF (PPZF), to balance SE and EH. A joint RIS phase-shift design and BS power-control problem is formulated to maximize the minimum harvested energy among energy users, solved via a block-coordinate-descent algorithm with SCA and quadratic-transform-based fractional programming; a double-layer penalty approach enforces rank-one RIS configurations. Numerical results show substantial harvested-energy gains (up to 132% in some setups) and reveal that pilot contamination can even boost EH, with RIS size enabling reductions in the required BS antenna count, highlighting practical energy efficiency benefits for RIS-enabled SWIPT systems.

Abstract

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 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.
Paper Structure (24 sections, 10 theorems, 88 equations, 8 figures, 3 algorithms)

This paper contains 24 sections, 10 theorems, 88 equations, 8 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Research Gap and Main Contributions
  4. Paper Organization and Notation
  5. System model
  6. Transmission Protocol
  7. Channel Estimation
  8. Signal Transmission
  9. Precoding Design and Performance Analysis
  10. Downlink Spectral Efficiency and Harvested Energy
  11. Partial Zero-Forcing Precoding
  12. Protective Partial Zero-Forcing
  13. Joint RIS Phase Shift Design and Power Control
  14. Optimization of $\boldsymbol{\rho}$ for Given $\boldsymbol{\theta}$
  15. Optimization of $\boldsymbol{\Theta}$ for Given $\boldsymbol{\rho}$
  16. ...and 9 more sections

Key Result

Proposition 1

The ergodic SE for the $k$-th IU, achieved by the PZF scheme, is given in closed-form by eq:SEk:Ex, where the effective SINR is given in eq:SINLPZF at the top of the next page, where $\mathcal{P}_k\subset\mathcal{K_I}$ is the set of IUs' indices sharing the same pilot with IU $k$.

Figures (8)

  • Figure 1: Illustration of RIS-assisted SWIPT massive MIMO system. EUs are blocked and are assisted by the RIS.
  • Figure 2: Frame structure for two-timescale transmission.
  • Figure 3: Performance of the PZF and PPZF versus the number of BS antennas ($K_I=5$, $K_E= 10$, $\mathrm{PRF^{\mathrm{I}}}=0$).
  • Figure 4: Performance of the PZF and PPZF versus the number of EUs ($K_I=5$, $N=225$, $M=150$, $\mathrm{PRF^{\mathrm{I}}}=0$).
  • Figure 5: Convergence behavior of Algorithm 1 and Algorithm 2 for different numbers of RIS elements ($K_I=5$, $K_E=10$, $\mathrm{PRF^{\mathrm{I}}}=0$, $\mathrm{PRF^{\mathrm{E}}}=9$).
  • ...and 3 more figures

Theorems & Definitions (21)

  • Remark 1
  • Proposition 1
  • proof
  • Corollary 1
  • proof
  • Proposition 2
  • proof
  • Corollary 2
  • proof
  • Remark 2
  • ...and 11 more