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End-to-end deep learning framework for key-free physical-layer security in WDM-RoF.

Yue Zhu, Jia Ye, Lianshan Yan

|September 23, 2025

Summary

This study presents a deep learning framework for secure WDM-RoF fronthaul, eliminating cryptographic keys. It ensures physical-layer security (PLS) through unique neural network pairings and channel characteristics.

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Area of Science:

Telecommunications Engineering
Network Security
Deep Learning Applications

Background:

Future networks require high-throughput and secure fronthaul solutions.
Existing security methods for WDM-RoF systems often rely on complex cryptographic keys.
Physical-layer security (PLS) offers a promising alternative for robust network protection.

Purpose of the Study:

To introduce an end-to-end (E2E) deep learning (DL) framework for PLS in WDM-RoF systems.
To achieve security without explicit cryptographic keys, reducing system complexity.
To demonstrate the effectiveness of DL-based PLS in WDM-RoF links.

Main Methods:

Developed a DL framework with jointly trained transmitter (TransNN) and receiver (ReceivNN) neural network pairs.

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Implemented dynamic interlocking between TransNN and ReceivNN pairs.

Ensured exclusive binding of each TransNN-ReceivNN pair to specific wavelength channel physical characteristics.

Main Results:

Simulations on a 4-wavelength WDM-RoF system over 20 km showed authorized connections maintained a bit error rate (BER) below the 7% forward error correction (FEC) threshold.
Mismatched model pairings resulted in BERs around 0.3.
Incorrect model-channel assignments led to BERs approaching 0.5, confirming security.

Conclusions:

The proposed E2E DL approach embeds security directly into the physical layer of WDM-RoF systems.
This method eliminates the need for key management, synchronization, and auxiliary encryption hardware.
It offers a lightweight yet robust PLS solution for future high-throughput and secure fronthaul networks.