Year: 2026
Type: article
Abstract: To address the phase noise issue in terahertz OFDM system, this paper proposes a dual-branch deep learning phase noise compensation network named AdaPhaseNet. The Transformer branch of this network leverages the powerful modeling capability of Transformers for long-range dependencies to achieve long-range phase noise estimation and compensation, while the CNN branch is employed for local signal enhancement. Finally, an optimized signal is output through a confidence-driven adaptive fusion module. For experimental validation of the algorithm, we constructed a photonic terahertz communication system comprising 10 km of fiber and 5 m of wireless transmission. Experimental results show that, compared with multiple baseline models, AdaPhaseNet achieves relative BER reductions ranging from 37.0% to 57.9% and EVM gains ranging from 1.4 dB to 3.2 dB.
Source: Electronics
Authors: Shenao Cai, Long Zhou, Tong Li, Jianguo Yu
Institution: Beijing University of Posts and Telecommunications
Language: English
Topic: Optical Network Technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G3678196313
OpenAlex ID: W7127132402