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Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications

Ji Wang, Suhong Luo, Yixuan Li, Wenwu Xie, Xingwang Li, Arumugam Nallanathan

TL;DR

This paper addresses energy-efficient downlink transmission in a 6G-like multi-user MIMO system by introducing a double self-sustainable RIS architecture that harvests energy to power RIS operation. It develops a joint optimization framework over BS beamforming ${\bf w_k}$, RIS phase shifts ${\bm{\Theta}}_1, {\bm{\Theta}}_2$, and amplitude coefficients $\beta_1, \beta_2$, aiming to minimize BS transmit power while satisfying SINR QoS constraints and RIS energy harvesting constraints. A block coordinate descent algorithm combining successive convex approximation and a penalty-based rank-one handling for RIS phase shifts solves the non-convex problem, with a closed-form-like result for optimal amplitudes (Proposition 1). Simulations demonstrate that double RISs yield lower BS power than conventional RIS setups, highlighting the practical potential for energy-efficient, self-sustained RIS-enabled wireless networks in future deployments.

Abstract

A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) is employed to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of two RISs. Simulation results show that the required power consumption at the BS for the proposed double self-sustainable RISs system is significantly reduced compared to conventional RIS systems.

Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications

TL;DR

This paper addresses energy-efficient downlink transmission in a 6G-like multi-user MIMO system by introducing a double self-sustainable RIS architecture that harvests energy to power RIS operation. It develops a joint optimization framework over BS beamforming , RIS phase shifts , and amplitude coefficients , aiming to minimize BS transmit power while satisfying SINR QoS constraints and RIS energy harvesting constraints. A block coordinate descent algorithm combining successive convex approximation and a penalty-based rank-one handling for RIS phase shifts solves the non-convex problem, with a closed-form-like result for optimal amplitudes (Proposition 1). Simulations demonstrate that double RISs yield lower BS power than conventional RIS setups, highlighting the practical potential for energy-efficient, self-sustained RIS-enabled wireless networks in future deployments.

Abstract

A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) is employed to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of two RISs. Simulation results show that the required power consumption at the BS for the proposed double self-sustainable RISs system is significantly reduced compared to conventional RIS systems.
Paper Structure (11 sections, 25 equations, 4 figures, 2 algorithms)

This paper contains 11 sections, 25 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model And Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Algorithm Design
  6. Optimizes ${\bf{w}}_k$ With Fixed ${{\bf{\Theta }}_1}$ and ${{\bf{\Theta }}_2}$
  7. Optimizes ${{\bf{\Theta }}_1}$ and ${{\bf{\Theta }}_2}$ With Fixed ${\bf{w}}_k$
  8. The Convergence and Computational Complexity
  9. Simulation Results
  10. Conclusions
  11. Proof of Proposition 1

Figures (4)

  • Figure 1: A double self-sustainable RISs assisted multi-user MIMO system.
  • Figure 2: Convergence of the proposed algorithm.
  • Figure 3: Transmission power versus the total number of reflection elements with $N_1 = N_2$.
  • Figure 4: Transmission power versus the reflection elements number of RIS1 with $N_1 + N_2 = 100$.