Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications (2008.01459v2)

Abstract: Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cram\'er-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

• The paper introduces innovative channel estimation algorithms that leverage PARAFAC decomposition to address RIS hardware constraints in multi-user MISO systems.
• It details iterative ALS and VAMP methods with theoretical validation via the Cramér-Rao Bound, demonstrating superior NMSE performance against benchmark schemes.
• Simulation results confirm enhanced channel estimation accuracy, paving the way for more efficient and robust RIS-assisted wireless communications.

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

The presented paper addresses the critical challenge of channel estimation (CE) in Reconfigurable Intelligent Surfaces (RIS)-empowered multi-user Multiple Input Single Output (MISO) systems. RISs, a candidate technology for beyond 5G wireless communications, facilitate low-power, high-speed, and massive connectivity through intelligent manipulation of electromagnetic waves. However, channel estimation remains a significant hurdle due to the large size and hardware constraints of RISs. This work proposes a novel CE framework exploiting Parallel Factor (PARAFAC) decomposition, introducing iterative algorithms for estimating channels between the Base Station (BS) and RIS, as well as users.

Technical Contributions

• Channel Estimation Algorithms: Two iterative algorithms are presented—one based on Alternating Least Squares (ALS) and the other on Vector Approximate Message Passing (VAMP). Both algorithms seek to iteratively reconstruct unknown channels by leveraging the unfolded forms of the cascaded channel model obtained through PARAFAC.
• Theoretical Analysis: The ALS algorithm’s performance is theoretically assessed by deriving its Cramér-Rao Bound (CRB), providing a benchmark for estimation accuracy. The results demonstrate that the ALS technique achieves CRB, validating the algorithm's effectiveness.
• Simulation Results: Extensive simulations highlight that the proposed algorithms outperform existing benchmark schemes, including the Least Squares Khatri-Rao Factorization (LSKRF) method, in terms of Normalized Mean Square Error (NMSE) performance across various system settings.

Implications and Future Directions

The paper's findings underscore the superior accuracy and robustness of the proposed CE techniques, paving the way for enhanced performance in RIS-assisted wireless systems. Notably, the benefit of employing PARAFAC decomposition in the estimation process provides a promising avenue for efficient handling of multi-dimensional data inherent in MISO systems.

These results have significant theoretical implications for future developments in RIS technologies, suggesting that detailed exploration of alternative decomposition methods could further enhance channel estimation processes. Practically, optimizing the RIS phase configurations through machine learning approaches could be a potential direction to explore, aimed at further reducing protocol overhead and improving CE accuracy in more complex deployments.

Conclusion

The proposed CE framework addresses a pressing need in RIS-empowered multi-user MISO communications, offering substantial improvements over existing solutions. With the increasing integration of RIS in next-generation networks, the algorithms presented in this paper provide a vital step towards addressing the specific challenges of channel estimation, setting the groundwork for future research focused on the practical implementation and optimization of RIS-based systems.