Resource constraints in quantum key distribution-secured optical networks (QKD-ONs) frequently prevent successful quantum light-path request (QLR) fulfillment. This work introduces a routing and resource assignment (RRA) strategy prioritizing minimal transmission distance to address this limitation. The proposed scheme employs a metric evaluating fiber length to determine optimal quantum signal paths. By minimizing cumulative (photon) travel distance, the proposed approach improves resource efficiency. The proposed distance-aware deep reinforcement learning (DA-DRL) based scheme’s performance is evaluated against first-fit (FF) and random-fit (RF) methods, across two network topologies NSFNET and UBN24. Simulation results show that, for NSFNET, the proposed approach scheme reduces blocking by 6.18% and 9.18% and for UBN24, by 2.71% and 3.41%, when compared to FF and RF respectively.
Resource constraints in quantum key distribution-secured optical networks (QKD-ONs) frequently prevent successful quantum light-path request (QLR) fulfillment. This work introduces a routing and resource assignment (RRA) strategy prioritizing minimal transmission distance to address this limitation. The proposed scheme employs a metric evaluating fiber length to determine optimal quantum signal paths. By minimizing cumulative (photon) travel distance, the proposed approach improves resource efficiency. The proposed distance-aware deep reinforcement learning (DA-DRL) based scheme’s performance is evaluated against first-fit (FF) and random-fit (RF) methods, across two network topologies NSFNET and UBN24. Simulation results show that, for NSFNET, the proposed approach scheme reduces blocking by 6.18% and 9.18% and for UBN24, by 2.71% and 3.41%, when compared to FF and RF respectively.
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.
Routing is a challenging problem in quantum key distribution (QKD)-secured optical networks (QKD-ONs) and involves the selection of an appropriate route that establishes a secure path between the QKD nodes for secret key distribution. Deep reinforcement learning (DRL) is a promising approach for solving decision-making problems in complex networking environments such as QKD-ONs. By leveraging the capabilities of DRL algorithms, the routing decisions can be optimized to enhance network performance. This paper proposes a DRL-based solution for routing in QKD-ONs that enables the routing agent to learn and adapt to changing network conditions by understanding the networking environment. The performance of the proposed scheme is compared with the baseline schemes on NSFNET in terms of blocking probability. Simulation results indicate that compared to the baseline schemes (shortest path (SP) and hop count (HC)), the proposed DRL-based routing scheme reduces the blocking by 14.31% and 8%, respectively.
Abstract Quantum key distribution (QKD) optical networks, relying on the information-theoretic security guaranteed by the fundamental laws of quantum physics, have become a core solution for ensuring data transmission security in critical fields such as finance and energy. However, QKD optical networks have inherent characteristics like severe resource constraints, dynamic changes in network environments. The collaborative optimization of routing, wavelength, and time-slot assignment (RWTA) has thus become a key bottleneck restricting their practical deployment. Traditional algorithms suffer from high complexity and poor adaptability in large-scale dynamic networks, while existing deep reinforcement learning (DRL) schemes insufficiently consider network security states, failing multi-objective co-optimization. Therefore, this paper proposes a multi-objective secure proximal policy optimization algorithm (MOS-PPO) based on DRL to solve the routing, wavelength, and time slots assignment problem in QKD optical networks. The MOS-PPO algorithm expands the state space by introducing a network security state vector that includes quantum bit error rate, risk coefficient, node capacity, and processing capability. It designs a multi-objective reward function, and establishes a security constraint mechanism to achieve the collaborative optimization of blocking probability (BP), resource utilization (RU), and security performance. Simulations on NSFNET and UBN24 topologies show MOS-PPO outperforms DRL-PPO algorithm, Security-aware Load balancing, first fit algorithm, and random fit algorithm. On NSFNET, average BP drops by 20.4%, 22.1%,28.4%, 36.7% and RU rises by 4.0%,3.9%, 22.2%, 31.0%, on UBN24, BP decreases by 9.3%, 11.4%, 18.8%, and 27.4% and RU increases by 3.8%, 21.2%, 10.3%, and 29.5%, with higher stable security margins. This study verifies the high adaptability and multi-objective optimization ability of the MOS-PPO algorithm in dynamic network environments, providing an efficient RWTA solution for the practical deployment of QKD optical networks.
The demand for secure communication in the age of quantum technologies has driven progress in quantum key distribution (QKD) techniques for optical networks. This research addresses the issues of high blocking probabilities (BPs) and the proper utilization of quantum resources in varying network loads by introducing a novel heuristic approach, termed dynamic security-aware quantum resource allocation (D-SQRA), designed for dynamic resource allocation in QKD-enabled optical networks. We propose two D-SQRA algorithms to employ an adaptive resource assignment (RA) strategy that concurrently addresses routing, wavelength, and time-slot selection while dynamically modifying security levels according to the real-time network load and resource availability. We evaluate the proposed D-SQRA performance against two conventional methods, namely, fixed security quantum resource allocation (F-SQRA) and baseline quantum resource allocation (B-QRA). We discuss the results for NSFNET and UBN24 topologies for network security performance metrics such as network security performance (NSP), BP, quantum key utilization (QKU), and time-slot utilization. The results show that the proposed D-SQRA algorithms provide significant improvement with respect to conventional techniques in addressing proper resource utilization and management by reducing BPs of the new incoming connection requests.
Resource Allocation in quantum key distribution-enabled optical networks (QKD-ON) is an NP-hard combinatorial problem. Classical heuristics are greedy and suffer high blocking under dynamic traffic and security constraints, while deep reinforcement learning (DRL) methods can reduce blocking but introduce a processing bottleneck because their inference latency is often slower than the operational timescales of QKD-ON. We introduce hybrid quantum–classical frameworks using the quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE) to perform request routing. The scheduling problem is encoded as a hamiltonian with penalty terms for wavelength and timeslot conflicts. We evaluate the approach on NSFNet and UBN topologies under dynamic traffic with time division multiplexing (TDM) and wavelength-time division multiplexing (WTDM). Our quantum-enhanced algorithms yield comparable or lower blocking with reduced inference latency, which can improve further on quantum hardware.
Quantum key distribution (QKD) has potential of providing long-term security as it is based on the fundamental principles of quantum mechanics. Security of information transmitted through optical networks has attracted attention recently due to its vulnerability to attacks post quantum computers. Hence, the integration of QKD into optical networks can significantly improve the security of existing and future optical networks. In QKD-secured optical networks, the quantum channel and the classical channel are integrated into a single optical fiber to reduce the deployment cost of the network infrastructure. However, due to limited network resources in a single optical fiber, the efficient provisioning of network resources (wavelengths and time slots) and blocking are two major issues in such networks. In this work, a resource assignment strategy with scheduling, namely, resource assignment with scheduling (RA-WS) has been proposed for utilization of network resources efficiently. In the RA-WS strategy, the QKD lightpath requests (QLPRs) have been served according to the proposed scheduling criterion. Moreover, the proposed scheduling criterion for routing, wavelength and time-slot assignment (RWTA) can reduce the effect of QLPRs blocking because of the limited network resources. The performance of the proposed RA-WS strategy has been analyzed in terms of blocking probability (BP), success probability of update secret key (SPUSK), and time-slot utilization ratio (TSUR) with different reserved wavelengths for quantum signal channel (QSCh), i.e.,WQand different time slots (ntr). Simulations performed on NSFNET network topology indicate that the RA-WS strategy performs better than the baseline strategy, i.e., resource assignment without scheduling (RA-WOS) in terms of all the considered metrics.