THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network
Year: 2025
Type: article
Abstract: To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems, a deep learning-based FPN-OAMP-SRLG algorithm was proposed. A feature extraction network SRLG was constructed by developing a deep residual block (BSRB) with coordinate attention and partial channel shift, along with a gated linear self-attention module (SARG). The channel estimation problem was formulated as an image restoration task through integration with the FPN-OAMP framework. The algorithm utilized pilot information, estimated via the least squares method, as input features and recovered channel state information through iterative linear and nonlinear estimators. Simulation results demonstrate that the proposed algorithm achieves high-precision THz channel estimation, exhibiting fast convergence and robust performance.
Authors: YU Shujuan, WEI Yuyao, CAI Lianglong, LU Hongyu, ZHANG Yun, ZHAO Shengmei
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7043815607
FWCI:
Cites:
Cited by:
Related to: