A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation (2002.10371v2)

Abstract: Intelligent surfaces comprising of cost effective, nearly passive, and reconfigurable unit elements are lately gaining increasing interest due to their potential in enabling fully programmable wireless environments. They are envisioned to offer environmental intelligence for diverse communication objectives, when coated on various objects of the deployment area of interest. To achieve this overarching goal, the channels where the Reconfigurable Intelligent Surfaces (RISs) are involved need to be in principle estimated. However, this is a challenging task with the currently available hardware RIS architectures requiring lengthy training periods among the network nodes utilizing RIS-assisted wireless communication. In this paper, we present a novel RIS architecture comprising of any number of passive reflecting elements, a simple controller for their adjustable configuration, and a single Radio Frequency (RF) chain for baseband measurements. Capitalizing on this architecture and assuming sparse wireless channels in the beamspace domain, we present an alternating optimization approach for explicit estimation of the channel gains at the RIS elements attached to the single RF chain. Representative simulation results demonstrate the channel estimation accuracy and achievable end-to-end performance for various training lengths and numbers of reflecting unit elements.

• The paper introduces a novel RIS architecture that minimizes active elements to perform explicit channel estimation.
• It employs an alternating optimization approach with sparse beamspace channel modeling to achieve significantly lower NMSE compared to traditional methods.
• Simulation results validate improved channel estimation accuracy and efficiency, highlighting a cost-effective path for next-generation wireless systems.

A Hardware Architecture for Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation

The paper presents a novel hardware architecture for Reconfigurable Intelligent Surfaces (RIS) with a focus on minimizing active elements required for explicit channel estimation. The proposed architecture is significant as it is poised to address key challenges associated with channel estimation in RIS-assisted communication systems. With the increasing need for advanced communication environments capable of supporting beyond 5G objectives, RISs have garnered attention due to their ability to manipulate electromagnetic (EM) fields through configurable unit elements. This proposed system leverages this capability while circumventing the extended training periods previously necessary due to the passive nature of RIS elements.

The architecture prioritizes minimalism, incorporating passive reflecting elements with a single Radio Frequency (RF) chain for baseband measurements. This configuration is instrumental in enabling explicit channel estimation, fundamentally transforming traditional RIS hardware designs reliant on metamaterials and subwavelength elements. By employing an alternating optimization approach, the system enhances precision in estimating channel gains directly at the RIS. The approach assumes sparse wireless channels in the beamspace domain, offering a pragmatic pathway for explicit estimation despite limited active elements.

The paper provides simulation results verifying the accuracy of channel estimation and the feasibility of end-to-end performance across varying training lengths and numbers of reflecting elements. One noteworthy finding is the significantly reduced Normalized Mean Squared Error (NMSE) achieved when compared to state-of-the-art algorithms like the Orthogonal Matching Pursuit (OMP) designed for Multiple Measurement Vectors (MMV), and the conventional Least Squares method. This accuracy is achieved even with fewer training symbols, which significantly enhances operational efficiency for deployments involving more RIS elements.

The proposed architecture is poised to influence practical and theoretical aspects of wireless communication. Practically, it offers a cost-effective solution to deploying programmable RIS environments, minimizing the need for extensive hardware modifications. Theoretically, the use of a sparse beamspace channel model and matrix completion techniques could drive future research in optimizing wireless networks, presenting a potential avenue for deeper exploration into advanced signal processing techniques that support sparse signal reconstruction.

Beyond current implications, the architecture's impact on AI in wireless communications could be substantial. The integration of minimal active hardware components with sophisticated algorithmic solutions might pave the way for the development of intelligent, self-configuring wireless environments capable of adapting to dynamic communication requirements. Such innovations could lead to breakthroughs in deploying AI-driven systems that perform complex channel estimation and environment control without human intervention.

The paper demonstrates that the proposed RIS architecture can achieve near-optimal channel estimation and data transmission rates comparable to scenarios assuming perfect channel knowledge. By utilizing fewer configurations and considering practical limitations in training and deployment, the findings suggest a pragmatic path forward for RIS implementations aligned with the efficiency demands of next-generation wireless communication systems.