Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems

TL;DR

This paper introduces a neural network-based approach to optimize phase allocations for channel estimation in RIS-aided systems, reducing pilot overhead by leveraging environmental structure and outperforming traditional methods.

Contribution

It proposes a joint neural network framework for phase optimization and channel estimation, improving efficiency and accuracy over existing techniques.

Findings

Neural network-based phase optimization outperforms brute-force DFT search.

The approach reduces the number of pilot sequences needed.

Learned phase allocations benefit various channel estimators.

Abstract

We consider channel estimation in systems equipped with a reconfigurable intelligent surface (RIS). In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS. However, for a given base station (BS) cell, there exist immanent structural characteristics of the environment which can be leveraged to reduce the necessary number of phase allocations. We verify this observation by a study on discrete Fourier transform (DFT)-based phase allocations where we exhaustively search for the best combination of DFT columns. Since this brute-force search is unaffordable in practice, we propose to learn a neural network (NN) for joint phase optimization and channel estimation because of the dependency of the optimal phase allocations on the channel estimator, and vice versa. We verify the effectiveness of the approach by numerical simulations where common choices for the phase allocations and the channel estimator are outperformed. By an ablation study, the learned phase allocations are shown to be beneficial in combination with a different state-of-the-art channel estimator as well.