LSTM-based equalizer with multi-head attention and gating mechanism for 2 × 2 MIMO photonics-assisted THz wireless system

Photonics-assisted terahertz (THz) communication, with its ultra-wide available bandwidth and good compatibility with fiber optic systems, is considered a key candidate solution to meet the requirements of 6G ultra-high speed, low latency, and large capacity transmission. However, it is susceptible to severe nonlinear damage during actual transmission. In this paper, we propose a long short-term memory (LSTM)-based equalizer with multi-head attention and gating mechanism (MHAG-LSTM). The model combines LSTM's modeling ability for temporal dependencies with the global modeling advantage of attention mechanism, and dynamically adjusts the weights of both in the equalized output through gating mechanism. The experiment demonstrated the 200 m outdoor wireless transmission of 50 GBaud polarization-division multiplexing (PDM) QPSK signal in a 140 GHz photonics-assisted THz system. The results indicate that under the same input optical power conditions, the MHAG-LSTM equalizer can achieve lower bit error rates (BER) than fully connected neural network (FCNN), convolutional neural network (CNN), LSTM, and Transformer equalizers. When the optical power into uni-traveling-carrier photodiode (UTC-PD) is 16 dBm, the BER of the received signals in the horizontal and vertical polarization directions are 3.64 × 10<sup>-3</sup> and 3.70 × 10<sup>-3</sup>, respectively, which are lower than the 7% hard decision forward error correction (HD-FEC) threshold of 3.8 × 10<sup>-3</sup>. Compared with CNN, LSTM, and Transformer, MHAG-LSTM reduces the average equalized BER of both polarization signals by 57.07% while reducing the number of real multiplications required to equalize per symbol (RMpS) by an average of 13.5%. MHAG-LSTM not only demonstrates superior nonlinear compensation capability in photonics-assisted THz communication, but also provides important reference for intelligent equalization and low complexity implementation in future 6G high-frequency high-capacity communication systems.

Authors

Institutions

Publication Details

Journal
Optics Express
Published
2025-11-26
DOI
https://doi.org/10.1364/oe.580821
Primary Topic
Advanced Photonic Communication Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LSTM-based equalizer with multi-head attention and gating mechanism for 2 × 2 MIMO photonics-assisted THz wireless system

Y. P. Xu, Siqi Wang, Gang Li, Jie Zhang et al.
Optics Express
Advanced Photonic Communication Systems
article

LSTM-based equalizer with multi-head attention and gating mechanism for 2 × 2 MIMO photonics-assisted THz wireless system

Y. P. Xu, Siqi Wang, Gang Li, Jie Zhang, Wen Zhou, Jianping Zhu, Xiaolong Pan
article en

Abstract

Photonics-assisted terahertz (THz) communication, with its ultra-wide available bandwidth and good compatibility with fiber optic systems, is considered a key candidate solution to meet the requirements of 6G ultra-high speed, low latency, and large capacity transmission. However, it is susceptible to severe nonlinear damage during actual transmission. In this paper, we propose a long short-term memory (LSTM)-based equalizer with multi-head attention and gating mechanism (MHAG-LSTM). The model combines LSTM's modeling ability for temporal dependencies with the global modeling advantage of attention mechanism, and dynamically adjusts the weights of both in the equalized output through gating mechanism. The experiment demonstrated the 200 m outdoor wireless transmission of 50 GBaud polarization-division multiplexing (PDM) QPSK signal in a 140 GHz photonics-assisted THz system. The results indicate that under the same input optical power conditions, the MHAG-LSTM equalizer can achieve lower bit error rates (BER) than fully connected neural network (FCNN), convolutional neural network (CNN), LSTM, and Transformer equalizers. When the optical power into uni-traveling-carrier photodiode (UTC-PD) is 16 dBm, the BER of the received signals in the horizontal and vertical polarization directions are 3.64 × 10<sup>-3</sup> and 3.70 × 10<sup>-3</sup>, respectively, which are lower than the 7% hard decision forward error correction (HD-FEC) threshold of 3.8 × 10<sup>-3</sup>. Compared with CNN, LSTM, and Transformer, MHAG-LSTM reduces the average equalized BER of both polarization signals by 57.07% while reducing the number of real multiplications required to equalize per symbol (RMpS) by an average of 13.5%. MHAG-LSTM not only demonstrates superior nonlinear compensation capability in photonics-assisted THz communication, but also provides important reference for intelligent equalization and low complexity implementation in future 6G high-frequency high-capacity communication systems.

Optics ExpressVol. 33(25)
Beijing Institute of Technology (CN), Fudan University (CN)
Openalex Percentile: Top 63%
Advanced Photonic Communication Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.