학술논문
Multi-Task Learning Based Channel Estimation for Hybrid-Field STAR-RIS Systems
Document Type
Conference
Author
Source
GLOBECOM 2023 - 2023 IEEE Global Communications Conference Global Communications Conference, GLOBECOM 2023 - 2023 IEEE. :6573-6578 Dec, 2023
Subject
Language
ISSN
2576-6813
Abstract
A joint cascaded channel estimation framework is proposed for simultaneously transmitting and reflecting recon-figurable intelligent surfaces (STAR-RIS) systems with hardware imperfection, in which practical the hybrid-field electromagnetic wave radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations in user domain and STAR-RIS element domain, we propose a multitask network (MTN) with multi-expert branches to simultaneously reconstruct the high-dimensional transmitting and reflecting channels from the observed mixture channel with noise. In the proposed MTN architecture, a learnable shrinkage module is exploited to constrict the communication noise, and self-attention mechanism-based Transformer layers are utilized to extract the nonlocal feature of the non-stationary cascaded channel. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.