Reliable low Earth orbit satellite networks (LEO-SNs) should be capable of optimally adapting to dynamic environments and remaining resilient against smart jamming attacks. In this context, dynamic inter-satellite link (ISL) routing is crucial for enabling efficient and adaptive data transmission across any satellite network during smart jamming attacks. However, due to the environmental variability, ISL routing becomes a complex time-sequential optimization problem. Accordingly, in this study, we propose a digital twin-assisted federated quantum deep reinforcement learning (DT-FQDRL) framework to solve dynamic ISL routing with faster convergence and minimal-error solutions. The DT-FQDRL framework optimizes ISL routing by minimizing jamming success rate and total delay while maximizing energy efficiency. Specifically, a digital twin (DT) replicates the LEO-SN environment to simulate satellite interactions and jamming behaviors at each time step. In this virtual setting, each satellite employs quantum deep reinforcement learning (QDRL) for local training and long-term prediction. To enhance data privacy and prevent node conflicts, a hierarchical federated learning scheme aggregates local QDRL models within the DT. The optimized weights are then transferred to real satellites. Our numerical results demonstrate that the DT-FQDRL framework reduces jamming success rate by 48.16%, decreases total delay by 22.26%, and improves energy efficiency by 6.17% over existing benchmarks.

In recent years, the demand for seamless connectivity and highly efficient, reliable network services for low earth orbit (LEO) satellites has escalated. To meet these expectations, a critical issue that must be addressed is combating malicious jamming attacks on satellite networks, which occur due to the open nature of satellite-ground connections. Moreover, in the era of artificial intelligence (AI), AI-based jamming poses a severe threat to the security of satellite networks and disrupts secure communications, particularly given the dynamic movements of LEO satellites and the time-sequential complexity of such attacks. Accordingly, this paper proposes a quantum adversarial deep reinforcement learning (QADRL) approach to mitigate AI-based jamming attacks while enhancing the quality-of-service (QoS) for LEO satellite networks. Specifically, the proposed QADRL approach is based on a zero-sum Markov game utilizing two opposing learning networks: one optimizing satellite routing links to avoid jamming and improve QoS, while the other, focuses on the jammer, optimizes the trajectory, jamming nodes, and power of unmanned aerial vehicles (UAVs) to maximize jamming success. The results demonstrate that the proposed QADRL outperforms classical adversarial DRL (CADRL) by reducing the jamming success rate by 33.33% and increasing the average QoS of the satellite network by 18.4975%.

Controlled quantum teleportation (CQT) is a key technique that allows quantum information to be transmitted over a quantum network under the control of a network administrator/ firewall. Existing CQT protocols rely on complex entanglement structures or increased number of qubits to enhance the administrator’s ability to block unauthorized communication, referred to as the control level, which poses practical challenges on current quantum hardware due to decoherence and the fragility of quantum states. In contrast, we propose efficient single and dual controller protocols that improve the control level, while using practical and experimentally feasible entanglement resources such as GHZ and GHZ-like states. The proposed protocols adopt quantum hiding to enhance the network’s control level, whether the receiver is compliant or non-compliant with the protocol. The dual-controller design further enables the integration of demilitarized zones (DMZs) into quantum networks, providing isolated intermediate regions jointly governed by two controllers and strengthening security boundaries. Our results demonstrate that the single-controller protocol achieves 87.5% control, reflecting a 75% improvement over the standard CQT scheme, while the dual-controller protocol achieves 98.44% control, corresponding to a 96.88% improvement. This improvement is achieved at a low cost of 2−9% reduction in the rate of successful teleportation when tested in noisy environments. In addition, both protocols maintain high state-averaged teleportation fidelity across randomly generated input states. When benchmarked against existing protocols, the proposed schemes demonstrate superior performance by achieving the highest efficiency while using the least amount of quantum resources.

Quantum networks (QNs) enable secure distributed quantum computing and sensing over next-generation optical communication networks by distributing entangled states over optical channels. However, quantum switches (QSs) in such QNs, which perform entanglement distribution, have limited resources, e.g., single-photon sources (SPSs) and quantum memories, which are sensitive to noise and losses. Efficient QS resource allocation is needed to minimize entanglement distribution delay. This paper proposes a QS resource allocation framework that jointly optimizes the average entanglement distribution delay and entanglement distillation operations to improve end-to-end (e2e) fidelity and meet user-specific rate and fidelity requirements. The proposed framework accounts for realistic QN noise and imperfections, deriving analytical expressions for quantum memory decoherence noise and resulting e2e fidelity after distillation. It also considers practical deployment factors, allowing QSs to control 1) nitrogen-vacancy (NV) center SPS types based on their isotopic decomposition, and 2) nuclear spin regions based on coupling strength and distance from NV center’s electron spin. The QS resource allocation optimization problem is solved using a simulated annealing algorithm. Simulation results show that the proposed framework manages to satisfy all users rate and fidelity requirements, unlike existing distillation-agnostic, minimal distillation, and physics-agnostic frameworks which do not perform distillation, perform minimal distillation, and do not control the physics-based NV center characteristics, respectively. Furthermore, the proposed framework results in significant reductions in the average e2e entanglement distribution delay, along with enhancements in the average e2e fidelity compared to the aforementioned existing frameworks.

Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users’ quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 84% in users’ end-to-end fidelities. The proposed framework also achieves a 63% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence.

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

The deployment of digital twins (DTs) at the edge of a wireless network can facilitate low-latency and high-throughput DT autonomous and real-time Internet of everything (IoE) applications. In such DT edge networks (DTENs), each DT has two types of real-time tasks that require timely processing: DT update tasks and DT inference tasks. However, the joint scheduling of these two types of tasks has been overlooked in prior works. In this paper, the first joint real-time scheduling scheme for DT update and inference tasks in a DTEN is proposed. Moreover, a novel performance metric called freshness is introduced to capture the effectiveness and synchronization performance of scheduling. Also, a new scheduling scheme is proposed to efficiently solve a freshness maximization problem for DTENs. Simulation results show that the performance of the proposed scheme is within 4% of the upper bound for DTENs with 20 physical objects, and within 12% of the upper bound in worst cases for DTENs with more than 30 physical objects. The results also show that the proposed approach reduces the maximum de-synchronization time by 63% compared to existing real-time scheduling algorithms.

Abstract The reliance on wireless services to exchange critical data is associated with various threats and attacks, which must be mitigated to ensure integrity and security of those wireless services. Posing a serious challenge to wireless systems, jamming is among these attacks. In order to mitigate jamming, directive antennas are used to minimize the signals that are received from the jammer, while maximizing the received legitimate signal from the authorized transmitter. In this paper, we propose a machine learning‐based anti‐jamming framework to provide a spatially dynamic and instantaneous anti‐jamming performance that is achieved at the physical layer. The proposed framework incorporates a dataset that can be deployed in the hardware of a receiver with a massive Multiple‐Inputs Multiple‐Outputs–(MIMO) antenna. Our extensive performance evaluation results demonstrate the effective performance of the proposed framework in preserving integrity of a massive–MIMO communication system despite the presence of a hostile jammer. Particularly, due to the tabular nature of the generated dataset, tree‐based random forest models achieved the best performance with a signal‐to‐interference‐plus‐noise ratio accuracy of and fast anti‐jamming response in around 1.66 s under sever jamming conditions.

In multi-cell multi-user multiple-input multiple-output (MC MU-MIMO) visible light communication (VLC) systems, each user is equipped with multiple closely-placed photodiodes (PDs) with similar channel gains, leading to severe inter-cell interference and intra-cell interference. To address this problem, this paper proposes a hybrid dimming (HD) scheme with MIMO VLC transceiver design, which jointly optimizes transmit and receive antenna selection (TRAS), cell clustering and precoding (TRASP-HD) for MC MU-MIMO VLC systems. In this scheme, a sum-rate maximization problem under the dimming level and illumination uniformity is formulated and solved by being divided into two sub-problems. In particular, The first sub-problem is on TRAS and cell formation based on the criterion of sum-rate maximization under the illumination uniformity constraint. With the same goal, the second sub-problem is on optimizing the precoding matrices of each cell. Finally, these two sub-problems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario, the mean bandwidth efficiency of TRASP-HD scheme is 2.36 bit/s/Hz higher than the conventional MC MU-MIMO system.

Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore complex machine learning problems. Recently, some QML models were proposed for performing classification tasks, however, they rely on centralized solutions that cannot scale well for distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quantum networks to allow for distributing quantum learning. This paper proposes the first fully quantum federated learning frame-work that can operate over purely quantum data. First, the proposed framework generates the first quantum federated dataset in literature. Then, quantum clients share the learning of quantum circuit parameters in a decentralized manner. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution, which is the first implementation combining Google’s TensorFlow Federated and TensorFlow Quantum.

Wirelessly connected devices play a vital role in people's daily life, especially with the significant rise in the number of devices connected to the Internet and the huge data being generated everyday. However, the open nature of the wireless channels makes them vulnerable to several threats. One of these major threats is jamming attacks which try to disrupt the reception of the useful signal by a receiver. In this paper, we propose a physical layer security anti-jamming method using massive planar antenna arrays. A receiver is assumed to perform anti-jamming against a single jammer trying to degrade the communication link between two parties. A large database of possible antenna array configurations with different radiation patterns is generated. Two methods are proposed for searching through the database. In the first, searching through the database gives the configuration with the deepest null towards the jammer, while in the second, we identify the configuration with the largest maximum to null ratio. The signal-to-interference-plus-noise-ratio is the chosen metric of performance for evaluating the chosen array configurations by both methods. Supporting simulation results validate the effectiveness of the proposed anti-jamming strategy.