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Performance evaluation of post quantum digital signature algorithms over WDM optical communication system

  • Marwa M. Hamood , Tayseer S. Atia and Arwa A. Moosa EMAIL logo
Published/Copyright: August 1, 2025
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Abstract

The advent of quantum computing necessitates the integration of post-quantum cryptography (PQC) into high-speed optical networks to safeguard against emerging threats. This study rigorously evaluates NIST-standardized PQC digital signature algorithms – CRYSTALS-Dilithium, FALCON, and SPHINCS+ – in wavelength-division multiplexing (WDM) systems, addressing the critical interplay between cryptographic robustness, file size dynamics, and optical network performance. Through comprehensive OptiSystem simulations, we analyze transmission fidelity (BER, Q-factor), power efficiency, and channel spacing impacts across single- and multi-channel WDM architectures. Key findings reveal that compact lattice-based schemes like FALCON-512 achieve high Q-factor at optimum power (−2) dBm for small files (≤45 bits), making them ideal for latency-sensitive applications such as real-time financial transactions. Conversely, large files (>200 bits) demand adaptive power allocation (−12 dBm–−10 dBm) and hybrid error correction (LDPC + FEC) when using SPHINCS + , despite its 41 kB signature overhead. Optimal channel spacing (0.8 nm for metro networks, 1.6 nm for long-haul links) balances spectral efficiency and resilience to distortions. The results underscore the necessity of context-aware cryptographic frameworks: FALCON-512 excels in low-power, high-security metro-scale deployments (50–100 km), while SPHINCS + suits archival data with high throughput tolerance. This work provides actionable insights for designing quantum-safe optical networks, harmonizing PQC standards with the evolving demands of high-capacity, low-latency infrastructure.


Corresponding author: Arwa A. Moosa, College of Engineering, Al-Iraqia University, Baghdad, Iraq, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-03-28
Accepted: 2025-06-14
Published Online: 2025-08-01

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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