[Paper Review] Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications

Songjie Yang, Chenfei Xie+2 more|arXiv (Cornell University)|Jul 28, 2022

Advanced Wireless Communication Technologies4 citations

TL;DR

This paper proposes a novel channel estimation framework for reconfigurable intelligent surface (RIS)-assisted cell-free massive MIMO systems, leveraging two key characteristics: shared BS-RIS and RIS-user channels across users and base stations. It introduces a 3D multiple measurement vector (3D-MMV) compressive sensing method for cascaded channel estimation and a multi-BS cooperative, pilot-reduced two-timescale estimation scheme, both showing significant performance gains in simulations with reduced pilot overhead and improved accuracy.

ABSTRACT

Recent research has focused on reconfigurable intelligent surface (RIS)-assisted cell-free systems with the goal of enhancing coverage and lowering the cost of cell-free networks. However, current research makes the assumption that the perfect channel state information is known. Channel acquisition is, certainly, a difficulty in this case. This work is aimed at investigating RIS-assisted cell-free channel estimation. Toward this end, two unique characteristics are pointed out: 1) For all users, a common channel exists between the base station (BS) and the RIS; and 2) For all BSs, a common channel exists between the RIS and the user. Based on these two characteristics, cascaded and two-timescale channel estimation concerns are studied. Subsequently, two solutions for tackling with the two issues are presented respectively: a three-dimensional multiple measurement vector (3D-MMV)-based compressive sensing technique and a multi-BS cooperative pilot-reduced methodology. Finally, simulations illustrate the effectiveness of the schemes we have presented.

Motivation & Objective

• Address the critical challenge of acquiring accurate channel state information (CSI) in RIS-assisted cell-free networks, where perfect CSI is typically assumed but unattainable in practice.
• Leverage two unique structural characteristics: (1) a common BS-RIS channel across all users, and (2) a common RIS-user channel across all base stations, to enable joint estimation.
• Develop a cascaded channel estimation method based on 3D-MMV compressive sensing to exploit multi-user and multi-BS correlation for improved estimation efficiency.
• Propose a pilot-reduced two-timescale channel estimation strategy using multi-BS cooperation to minimize training overhead while maintaining accuracy.
• Demonstrate the effectiveness of the proposed schemes through simulations under realistic system parameters and channel models.

Proposed method

• Formulate cascaded channel estimation as a 3D-MMV problem by exploiting the shared BS-RIS channel across users, enabling joint estimation of angle-of-departure (AoD) components.
• Design a 3D-MLAOMP algorithm using tensor contraction to efficiently solve the 3D-MMV problem, incorporating look-ahead orthogonal matching pursuit for improved sparsity recovery.
• Model the two-timescale channel estimation by separating slow-varying BS-RIS and fast-varying RIS-user links, leveraging the time-scale difference for pilot optimization.
• Construct a multi-BS cooperative sensing matrix to enable joint pilot transmission and reception across all base stations, reducing total pilot overhead.
• Utilize compressive sensing principles to recover sparse channel components from fewer measurements, based on the sparsity of wireless channels and the Kronecker structure of the RIS response.
• Integrate the Moore-Penrose pseudoinverse and constrained optimization to solve the underdetermined system in the estimation process.

Experimental results

Research questions

• RQ1 How can shared channel characteristics (common BS-RIS and RIS-user links) across multiple users and base stations be exploited to improve channel estimation efficiency in RIS-assisted cell-free systems?
• RQ2 Can a 3D-MMV-based compressive sensing framework effectively estimate cascaded channels in multi-user, multi-BS RIS systems with reduced pilot overhead?
• RQ3 To what extent can multi-BS cooperation reduce pilot overhead in two-timescale channel estimation while maintaining estimation accuracy?
• RQ4 How does the proposed 3D-MLAOMP algorithm compare to conventional 1D and MMV-based methods in terms of normalized mean square error (NMSE) and robustness to noise?
• RQ5 What is the performance gain of the proposed pilot-reduced, cooperative two-timescale estimation scheme compared to individual-BS estimation in realistic fading environments?

Key findings

• The proposed 3D-MLAOMP algorithm achieves significantly lower normalized mean square error (NMSE) than conventional 1D OMP and LAOMP methods, especially at low SNR, due to joint multi-user and multi-BS estimation.
• With 32 measurements, the 3D-MLAOMP method achieves an NMSE performance close to the theoretical lower bound (Oracle LS), demonstrating high estimation accuracy.
• The multi-BS cooperative two-timescale estimation scheme reduces pilot overhead by enabling joint estimation across all base stations, outperforming individual-BS estimation in terms of NMSE.
• Simulation results show that the proposed pilot-reduced scheme achieves better NMSE performance than individual-based schemes, even with fewer pilots, due to cooperative diversity and shared channel knowledge.
• The 3D-MMV framework effectively exploits the sparsity and structure of the cascaded channel, enabling accurate estimation with minimal training overhead in multi-user RIS-assisted cell-free systems.
• The two-timescale estimation approach successfully leverages the slow time-varying nature of the BS-RIS channel and fast fading of RIS-user links, enabling efficient pilot allocation and improved estimation efficiency.