[Paper Review] Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

Zhaohui Yang, Mingzhe Chen+5 more|arXiv (Cornell University)|May 1, 2020

Advanced Wireless Communication Technologies41 references58 citations

TL;DR

The paper proposes joint optimization of transmit beamforming, RIS phase shifts, and RIS on-off status across distributed RISs to maximize energy efficiency, using SCA and dual/greedy methods for single- and multi-user scenarios.

ABSTRACT

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

Motivation & Objective

• Motivate energy-efficient design for next-generation wireless networks with distributed RISs.
• Maximize energy efficiency under user minimum rate constraints and power limits.
• Dynamically control RIS on-off status and phase shifts to balance performance and energy cost.
• Develop scalable algorithms for single-user and multi-user cases.

Proposed method

• Model a downlink MISO system with one BS, multiple distributed RISs, and multiple users.
• Introduce binary RIS on-off variables and unit-modulus RIS phase shift matrices to capture practical power consumption.
• Formulate a joint optimization problem maximizing EE: R_t/P_t with rate and power constraints (MINLP).
• For K=1, solve with alternating steps: (i) joint phase/power optimization using SCA with closed-form phase update and power in closed form; (ii) RIS on-off via dual method.
• For K>1, propose a low-complexity greedy on-off search and iterative solution using SCA for phase/beamforming and a dual-based approach for on-off.
• Provide closed-form EE solution for the single-user power and a dual-based, relaxed-to-integer approach for the multi-user RIS on-off problem.

Experimental results

Research questions

• RQ1 How can energy efficiency be maximized in a network with distributed RISs under per-user rate constraints?
• RQ2 What is the impact of turning RISs on/off on EE and how to optimally select which RISs to activate?
• RQ3 How do phase shift optimization and beamforming interact with RIS activation to maximize performance in single-user versus multi-user scenarios?

Key findings

• The proposed scheme achieves up to 33% EE gains in the single-user case over conventional RIS schemes.
• The proposed scheme achieves up to 68% EE gains in the multi-user case over amplify-and-forward relays.
• SCA provides a locally optimal phase solution with a proven convergence under the single-user algorithm.
• A closed-form optimal transmit power is derived for the single-user EE optimization.
• For multi-user, a low-complexity greedy on-off method and a dual method (with relaxed integer constraints) determine RIS activation while preserving EE.
• Numerical results validate the effectiveness of distributed RISs in enhancing energy efficiency compared to baselines.