82.5 GHz Photonic W-Band IM/DD PS-PAM4 Wireless Transmission over 300 m Based on Balanced and Lightweight DNN Equalizer Cascaded with Clustering Algorithm

TL;DR

This paper presents a new wireless communication system using a lightweight neural network and clustering algorithm to improve signal quality and reduce complexity in high-frequency wireless transmission.

Contribution

A lightweight DNN equalizer combined with SMOTE and clustering is introduced to enhance performance and reduce complexity in W-band PAM4 wireless transmission.

Findings

The proposed system achieved a bit error rate of 1.32 × 10−3 over 300 m W-band transmission.

The new equalizer reduced algorithm complexity by 70.6% and training sequence length by 33%.

The method improved performance by 70.7% compared to conventional schemes.

Abstract

With the rise of 6G, the exponential growth of data traffic, the proliferation of emerging applications, and the ubiquity of smart devices, the demand for spectral resources is unprecedented. Terahertz communication (100 GHz–3 THz) plays a key role in alleviating spectrum scarcity through ultra-broadband transmission. In this study, terahertz optical carrier-based systems are employed, where fiber-optic components are used to generate the optical signals, and the signal is transmitted via direct detection in the receiver side, without relying on fiber-optic transmission. In these systems, deep learning-based equalization effectively compensates for nonlinear distortions, while probability shaping (PS) enhances system capacity under modulation constraints. However, the probability distribution of signals processed by PS varies with amplitude, making it challenging to extract useful information from the minority class, which in turn limits the effectiveness of nonlinear equalization. Furthermore, in IM-DD systems, optical multipath interference (MPI) noise introduces signal-dependent amplitude jitter after direct detection, degrading system performance. To address these challenges, we propose a lightweight neural network equalizer assisted by the Synthetic Minority Oversampling Technique (SMOTE) and a clustering method. Applying SMOTE prior to the equalizer mitigates training difficulties arising from class imbalance, while the low-complexity clustering algorithm after the equalizer identifies edge jitter levels for decision-making. This joint approach compensates for both nonlinear distortion and jitter-related decision errors. Based on this algorithm, we conducted a 3.75 Gbaud W-band PAM4 wireless transmission experiment over 300 m at Fudan University’s Handan campus, achieving a bit error rate of 1.32 × 10−3, which corresponds to a 70.7% improvement over conventional schemes. Compared to traditional equalizers, the proposed new equalizer reduces algorithm complexity by 70.6% and training sequence length by 33%, while achieving the same performance. These advantages highlight its significant potential for future optical carrier-based wireless communication systems.

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