Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO (1912.09025v4)

Abstract: Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in an RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.

• The paper presents a matrix-calibration approach that reformulates cascaded channel estimation as a matrix factorization problem.
• It develops a message-passing algorithm leveraging channel sparsity and variations to reduce training overhead while maintaining high accuracy.
• Simulation results validate the theoretical performance bounds, highlighting energy efficiency and cost-effectiveness in RIS-assisted communication.

Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO

The paper "Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO" addresses a fundamental problem in the field of 6G networks, where reconfigurable intelligent surfaces (RIS) play a critical role. The research focuses on channel estimation challenges in RIS-assisted multiuser MIMO systems, which are pivotal for realizing efficient communication networks with reduced hardware costs and lower energy consumption compared to traditional massive MIMO systems.

Key Contributions

The authors thoroughly investigate the complex problem of estimating cascaded channels in an RIS-assisted multiuser MIMO system. The main contributions of the paper can be summarized as follows:

1. Channel Model and Problem Formulation: The authors model the RIS-to-BS and user-to-RIS channels using a Rician fading model, considering slow-varying and fast-varying components. They highlight the challenge of acquiring CSI in systems employing purely passive RIS elements, which cannot estimate channels due to their nature. The channel estimation problem is recast as a matrix-calibration based matrix factorization task.
2. Message-Passing Algorithm: To reduce the prohibitive training overhead traditionally required by channel estimation methods, the authors devise a novel message-passing algorithm tailored for the RIS-assisted system. This algorithm leverages the sparsity and slow variation of channels, making it computationally efficient without sacrificing accuracy.
3. Theoretical Performance Analysis: The research includes an analytical framework to derive the theoretical performance bounds of the estimator in a large-system limit using the replica method. The performance analysis involves intricate statistical physics concepts, projecting the estimator's potential in ideal conditions.
4. Numerical Results: The paper validates the proposed message-passing algorithm through simulation, demonstrating superior accuracy and efficiency. The results show close alignment with the analytical predictions based on the replica method, underscoring the practical viability of the approach.

Implications and Future Directions

The implications of this research are multifaceted:

• Practical Applications: The proposed channel estimation technique enables the deployment of RIS technology in real-world scenarios by overcoming channel acquisition challenges, thereby optimizing the configuration of RIS to improve communication link quality.
• Theoretical Advancements: By employing advanced mathematical tools such as the replica method, the research opens avenues for leveraging statistical physics in evaluating performance limits in wireless communications, particularly in systems characterized by large matrices and complex interdependencies.
• Energy Efficiency and Cost Reduction: The findings advocate the adoption of RIS as a viable technology for future wireless networks, focusing on enhancing energy efficiency and reducing operational costs relative to massive MIMO systems.

Moving forward, this work may inspire further investigations into enhancing the robustness of message-passing algorithms under varying environmental conditions and advanced channel models. Additionally, exploring RIS configurations in dynamically changing environments where the slow and fast varying channels continuously evolve could yield more adaptive and resilient communication systems.

In sum, this paper makes a significant contribution to the foundational aspects of future wireless networks, presenting a solid step toward achieving efficient, sustainable, and scalable wireless communication solutions with RIS technology.