Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface

Core Concepts

The authors propose an efficient algorithm for joint base station and reconfigurable intelligent surface beamforming optimization to minimize transmit power while meeting user quality-of-service constraints.

Abstract

The paper discusses the use of reconfigurable intelligent surfaces (RIS) in downlink multi-user transmission scenarios. It presents algorithms for optimizing beamforming at both the base station and RIS, demonstrating improved performance and computational efficiency compared to existing methods. RIS technology offers advantages over traditional options, making it a promising solution for various wireless applications.
In the context of multicast beamforming, the paper introduces an alternating multicast beamforming (AMBF) algorithm that efficiently solves subproblems related to base station and RIS beamforming. The study also extends its approach to unicast scenarios, providing semi-closed-form updates for solutions. By exploring the structure of quality-of-service problems, the authors demonstrate a scalable and effective method for downlink multi-user beamforming design with RIS assistance.
The proposed algorithms are shown to be effective in terms of performance and computational cost through simulation results, offering a promising solution for next-generation wireless systems.

Stats

The computational complexity of the AMBF algorithm grows linearly with the number of RIS elements and BS antennas.
The penalty weight ζ is used in the relaxed problem eSw.
The complexity of PSA grows linearly over M, N, and Ktot.
The overall computational complexity in each AO iteration of Algorithm 1 is as low as O(K2tot + KtotMN).

Quotes

Key Insights Distilled From

Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface

by Mohammad Ebr... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2401.00594.pdf

Deeper Inquiries

How does the proposed AMBF approach compare to existing methods in terms of scalability

The proposed Alternating Multicast Beamforming (AMBF) approach in the context provided offers scalability advantages compared to existing methods. The AMBF algorithm breaks down the optimization problem into two subproblems - one for the base station (BS) multicast beamforming and another for the Reconfigurable Intelligent Surface (RIS) passive multicast beamforming. By solving these subproblems alternately, the computational complexity grows linearly with the number of RIS elements and BS antennas. This means that as the system scales up with more elements and antennas, the AMBF approach remains efficient and manageable in terms of computational resources.

What are the implications of using reconfigurable intelligent surfaces (RIS) in future wireless communication systems

Reconfigurable Intelligent Surfaces (RIS) have significant implications for future wireless communication systems. These surfaces can actively control and enhance wireless propagation channel conditions by adjusting phase shifts of reflected signals towards desired directions with minimal energy consumption. The use of RIS technology can lead to improved coverage extension, enhanced capacity, reduced interference, increased spectral efficiency, better signal quality, and overall performance optimization in wireless networks.

How might advancements in RIS technology impact coverage extension and capacity enhancement

Advancements in Reconfigurable Intelligent Surface (RIS) technology are expected to have a profound impact on coverage extension and capacity enhancement in future wireless communication systems. By strategically deploying RIS elements within a network infrastructure, it becomes possible to manipulate signal reflections effectively to optimize coverage areas where traditional transmission may face limitations due to obstacles or interference issues. This targeted reflection control enables improved signal strength at specific locations without requiring additional power from transmitters or increasing antenna density significantly. As a result, RIS technology has great potential to revolutionize how coverage is extended efficiently while enhancing overall network capacity through intelligent signal manipulation techniques.