[Paper Review] Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Chongwen Huang, Alessio Zappone+3 more|arXiv (Cornell University)|Oct 16, 2018

Advanced Wireless Communication Technologies65 references3,715 citations

TL;DR

This paper proposes two low-complexity, convergent algorithms for energy efficiency (EE) optimization in RIS-assisted multi-user MIMO downlink systems, jointly optimizing transmit power and RIS phase shifts under QoS and power constraints. The methods achieve up to 300% higher EE than traditional amplify-and-forward relaying by leveraging sequential fractional programming and gradient descent with alternating maximization on a realistic RIS power model.

ABSTRACT

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Motivation & Objective

• Address the energy efficiency challenge in 5G and beyond networks by exploring RIS as a low-power alternative to traditional relays.
• Formulate a realistic RIS power consumption model dependent on number of reflecting elements and phase resolution.
• Optimize joint transmit power allocation and RIS phase shifts to maximize EE under individual user QoS and transmit power constraints.
• Develop computationally affordable, provably convergent algorithms for non-convex EE optimization in RIS-aided systems.
• Evaluate performance in a realistic outdoor environment, contrasting RIS with conventional AF relaying.

Proposed method

• Propose a realistic RIS power model based on number of reflecting elements and their phase resolution capability.
• Formulate a non-convex EE maximization problem with unit-modulus constraints on RIS phase shifts and QoS guarantees for users.
• Develop two alternating maximization-based algorithms: one using gradient descent for RIS phase shifts and fractional programming for power allocation; the other using sequential fractional programming for RIS optimization.
• Apply conjugate gradient search to refine phase shift optimization in the gradient-based algorithm.
• Integrate power constraints and minimum rate requirements into the optimization framework to ensure QoS.
• Benchmark performance against full power allocation and sum spectral efficiency maximization strategies in realistic outdoor simulations.

Experimental results

Research questions

• RQ1 Can RIS-based systems achieve significantly higher energy efficiency than traditional amplify-and-forward relays in outdoor cellular networks?
• RQ2 What is the optimal trade-off between RIS size (number of elements) and energy efficiency, considering both rate gain and hardware power consumption?
• RQ3 How do phase resolution and number of reflecting elements affect the achievable EE in RIS-aided systems?
• RQ4 Can low-complexity, convergent algorithms effectively solve the non-convex EE optimization problem with unit-modulus constraints on RIS phase shifts?
• RQ5 How does the performance of RIS-based systems scale with the number of users and base station antennas in realistic outdoor scenarios?

Key findings

• The proposed SFP-based algorithm achieves near-optimal spectral efficiency, closely matching results from exhaustive global optimization.
• The RIS-based system achieves up to 300% higher energy efficiency compared to traditional amplify-and-forward relay systems.
• Energy efficiency initially increases with the number of RIS elements but eventually decreases due to rising hardware power consumption, indicating an optimal RIS size exists.
• For small-to-moderate RIS sizes, EE improves with more elements, but beyond a threshold, the increased power cost outweighs the spectral gain.
• The optimal number of RIS elements depends on system parameters such as user count, base station antenna count, and individual RIS element power consumption.
• The performance gap between the proposed algorithms and global optimization is minimal, validating the effectiveness of the low-complexity design.