Semi-Blind Channel Estimation for Beyond Diagonal RIS

TL;DR

This paper introduces a semi-blind, data-driven joint channel and symbol estimation method for beyond diagonal RIS that eliminates the need for pilot sequences, using a tensor-based model and alternating estimation scheme.

Contribution

It proposes a novel semi-blind estimation algorithm for beyond diagonal RIS using a PARATUCK tensor model, reducing pilot dependence and providing decoupled channel estimates.

Findings

Demonstrates effective performance in selected system setups.

Shows competitive symbol error rate compared to perfect channel knowledge.

Validates the tensor-based approach through numerical simulations.

Abstract

The channel estimation problem has been widely discussed in traditional reconfigurable intelligent surface assisted multiple-input multiple-output. However, solutions for channel estimation adapted to beyond diagonal RIS need further study, and few recent works have been proposed to tackle this problem. Moreover, methods that avoid or minimize the use of pilot sequences are of interest. This work formulates a data-driven (semi-blind) joint channel and symbol estimation algorithm for beyond diagonal RIS that avoids a prior pilot-assisted stage while providing decoupled estimates of the involved communication channels. The proposed receiver builds upon a PARATUCK tensor model for the received signal, from which a trilinear alternating estimation scheme is derived. Preliminary numerical results demonstrate the proposed method's performance for selected system setups. The symbol error rate performance is also compared with that of a linear receiver operating with perfect knowledge of the cascaded channel.