[Paper Review] Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems

Jie Chen, Ying‐Chang Liang+2 more|arXiv (Cornell University)|Dec 8, 2019

Advanced Wireless Communication TechnologiesEngineering45 references133 citations

TL;DR

This paper proposes a two-step multi-user joint channel estimation framework for RIS-aided MU mmWave MIMO systems, leveraging common BS-RIS channels and two-dimensional sparsity to reduce training overhead via compressive sensing techniques.

ABSTRACT

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.

Motivation & Objective

• Motivate and address the challenge of channel acquisition in RIS-aided multi-user mmWave MIMO systems.
• Propose a sparse, two-step estimation protocol that exploits common BS-RIS channels across users.
• Develop a joint sparse recovery algorithm that leverages row-column-block sparsity in cascaded channels.
• Analyze convergence and complexity of the proposed algorithm and design training reflections to minimize mutual coherence.

Proposed method

• Model the cascaded RIS-aided channels as a two-dimensional sparse matrix with row-column-block sparsity.
• Represent cascaded channels in a virtual angular domain using dictionaries for RIS and BS arrays.
• Propose a two-step estimation: (i) use a subspace approach to exploit common column-block sparsity and estimate the common subspace, (ii) perform multi-user joint sparse matrix recovery accounting for common BS-RIS channel.
• Formulate the second step as a sparsity-promoting optimization using a log-sum penalty and solve via alternating optimization and iterative reweighted techniques.
• Incorporate training design that minimizes mutual coherence of the equivalent dictionary to improve estimation.
• Provide convergence, complexity, and initialization analysis for the proposed algorithm.

Experimental results

Research questions

• RQ1 How can we efficiently estimate cascaded BS-RIS-user channels in RIS-aided multi-user mmWave MIMO under sparsity?
• RQ2 Can the common BS-RIS channel across users be exploited to reduce training overhead and improve joint channel recovery?
• RQ3 What sparsity structure best characterizes cascaded channels in RIS systems and how can it be leveraged in recovery algorithms?
• RQ4 How should RIS reflection training sequences be designed to minimize mutual coherence and enhance estimator performance?
• RQ5 What are the convergence and complexity implications of the proposed two-step joint estimation approach?

Key findings

• Cascaded channels exhibit two-dimensional row-column-block sparsity, enabling sparse recovery approaches.
• Exploiting a common BS-RIS channel across users improves efficiency and estimation accuracy in a multi-user setting.
• A two-step procedure combining a subspace-based common column sparsity estimation with a joint sparse matrix recovery yields effective cascaded channel estimates.
• A log-sum sparsity promoting framework with alternating optimization effectively recovers the sparse X_k matrices representing cascaded channels.
• Training reflection coefficient design can reduce coherence and improve estimator performance, with analyzed convergence and complexity.