Two-stage method improves channel estimation for metasurface antennas
Two-Stage Refinement Sparse Channel Estimation for Reconfigurable Intelligent Metasurface Antenna (RIMSA) Massive MIMO
Information Theory
Summary
Wireless systems use many antennas to send data faster and in greater amounts. To do this well, they need to understand how signals travel between the antennas and the receiver, which is called channel estimation. The authors propose a two-step method to improve this estimation for a special kind of antenna called a reconfigurable intelligent metasurface antenna (RIMSA). First, they collect data in a way that simplifies the problem but creates some guessing errors. Then, they adjust the antenna settings to fix those guesses and get a more accurate estimate. This approach boosts accuracy compared to older methods.
What this means in practice
- •For wireless network engineers: Improve antenna array calibration in massive MIMO systems using two-stage channel estimation to enhance data throughput and reliability.
- •For antenna system designers: Optimize phase settings in metasurface antenna arrays to reduce signal ambiguity and improve direction-of-arrival accuracy.
Authors
Yakun Ma, Hui-Ming Wang, Jiaping He, Qingli Yan
Abstract
To meet the increasing demands for high data rates and large capacity, next generation wireless communication systems require transceivers equipped with a large number of antennas. Massive multiple-input multiple-output (MIMO) with metasurface antennas has emerged as a promising solution. In this paper, we investigate the channel estimation problem for the emerging reconfigurable intelligent metasurface antenna (RIMSA) array systems. Specifically, we develop a two-stage refinement (TSR) channel estimation method based on the compressed sensing (CS) principle. In the first stage, we exploit the antenna structure of RIMSA to receive pilots by setting identical phase response vectors across all RIMSAs. In this manner, the coherence of the measurement matrix under the CS framework is reduced and the channel estimation performance is improved. However, this special design introduces channel direction-of-arrival (DoA) estimation ambiguity and yields an ambiguous candidate DoA set. In the second stage, we optimize the phase responses of the metamaterial elements to resolve the ambiguity and accurately estimate the DoAs and channel coefficients. Overall, the estimation accuracy is improved in the first stage at the cost of ambiguity, and this ambiguity is eliminated in the second stage. We illustrate the performance advantages of the TSR method by presenting the numerical results and comparing it with the existing methods.