Opportunistic QKD: Exploiting Idle Capacity of Classical WDM Systems

TL;DR

This paper introduces an opportunistic QKD framework that leverages idle spectral capacity in classical WDM systems, enabling secure key distribution alongside classical traffic with optimized buffer management.

Contribution

It proposes a stochastic traffic model and a key reservoir model to maximize spectrum reuse for QKD while maintaining service reliability and SLA compliance.

Findings

45-65% of unused spectrum can be repurposed for QKD.

A heavy-tailed distribution models first-passage time effectively.

Trade-offs between buffer reset levels and recovery times are quantified.

Abstract

While Quantum Key Distribution (QKD) has been proven in lab environments, large-scale implementation requires integration with existing infrastructure. This paper proposes an opportunistic QKD framework that takes advantage of idle spectral capacity, that is, unused channels in classical fibers, to perform QKD while prioritizing classical traffic. To mitigate crosstalk during the co-propagation of classical and quantum signals, we require a guardband of unused channels between classical and quantum signals. We propose a stochastic traffic model, with a deterministic day-night cycle and fractional Gaussian noise. Monte-Carlo simulations of an 80-channel WDM system with our stochastic traffic model demonstrate that 45-65% of unused spectrum can be repurposed for QKD, depending on the traffic conditions. We also model a key reservoir model, with Available and Recovery states. We define the Reliability Horizon as the 3{\sigma} depletion threshold. We find a trade-off between buffer reset levels: increasing the buffer reset level extends the reliability horizon but linearly increases recovery time, resulting in longer service "dark windows". Furthermore, simulations indicate that the first-passage time follows a heavy-tailed distribution, which is accurately characterized by a composite model combining a diurnal trend and a Bihill transition function. This framework enables network operators to optimize buffer parameters for specific Service Level Agreements (SLAs) in real-world environments.