Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces (2005.00269v1)

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.

• The paper introduces novel iterative algorithms for distributed RIS optimization to maximize energy efficiency under minimum rate constraints.
• It employs Successive Convex Approximation, Dinkelbach's method, and alternating optimization to jointly configure phase shifts, beamforming, and RIS on-off statuses.
• Numerical results demonstrate that the DRIS approach outperforms traditional central RIS and AF relay schemes, highlighting significant energy efficiency improvements.

Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

This paper presents a rigorous exploration of energy-efficient resource allocation strategies for wireless communication networks augmented with Distributed Reconfigurable Intelligent Surfaces (DRISs). The paper focuses on optimizing network performance by jointly configuring the phase shifts, transmit beamforming, and on-off status of multiple RISs to enhance energy efficiency under specific minimum rate constraints for users.

Key Contributions and Methodology

The authors establish a framework where RISs are strategically distributed across a network to aid wireless communications by manipulating reflective properties of wireless signals. The primary objective is to optimize the energy efficiency, primarily defined by the ratio of the system's spectral efficiency to its overall power consumption. This is a challenging task due to the intrinsic non-linearity and mixed-integer nature of the resource allocation problem faced in such networks.

To tackle this, two iterative algorithms have been proposed catering to single-user and multi-user scenarios:

1. Single-User Scenario: The formulated problem involves optimizing the phase shifts, power allocation, and RIS on-off statuses to maximize energy efficiency. The authors propose a novel suboptimal solution using a Successive Convex Approximation (SCA) method to solve the phase optimization, with the power optimization resolved in closed form using Dinkelbach's method. The RIS on-off status is decided by a dual method that involves solving a series of relaxed optimization problems.
2. Multi-User Scenario: The complexity increases as multiple users are considered. The authors address this using an alternating optimization algorithm that iteratively solves subproblems of phase optimization, beamforming, and RIS on-off status using both SCA and a mixed-integer programming approach.

Strong Numerical Results

Significant numerical insights are presented, indicating that the DRIS scheme offers considerable advancements over traditional schemes such as central RIS deployments and Amplify-and-Forward (AF) relay schemes. Specifically, simulation results demonstrate up to 33% and 68% gains in energy efficiency for single and multi-user scenarios, respectively, compared to these traditional methods. Such results highlight the potential of distributed strategies in enhancing energy efficiency and coverage in wireless networks.

Implications and Future Directions

The research opens several avenues for future exploration. While the proposed algorithms provide suboptimal solutions with lesser complexity, the quest for global optimization in such a framework continues. A practical direction could involve the implementation and testing in real-world scenarios or extending the DRIS approach under varying environmental conditions and network topologies.

Furthermore, as the field evolves, the integration of machine learning techniques for adaptive optimization of RIS configurations in real-time presents a promising research direction. This paper lays a foundation for utilizing DRIS not merely as a theoretical construct but as a pivotal component in realizing energy-efficient, resilient next-generation wireless communication networks.