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We compare the sensitivity of fibre interferometers to vibrations using microwave oscillators and state of the art lasers over distances up to 70 km. High spectral purity lasers outperform only above 10 Hz, highlighting microwave advantages at low frequencies and the potential of hybrid microwave optical interferometry systems
Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous configurations. This paper interprets the protocol configuration anomaly detection problem as detection of structural inconsistencies of connected nodes and edges in a bipartite graph that captures both physical network entities and logical protocol states. This graph structural inconsistency detector (GSID) model is proposed to solve the problem efficiently. To handle the heterogeneous nature of protocol configuration parameters, GSID employs an adaptive configuration encoder (ACE) that dynamically selects encoding strategies per parameter to preserve fine-grained numerical discrepancies. To expose the subtle inconsistencies of connected nodes and edges in the bipartite graph, GSID uses an inconsistency dynamic attention (IDA) mechanism that scores edges by drawing asymmetric attentions from both ends, rule compliance from one end and route connectivity from the other. It is demonstrated experimentally that GSID outperforms state-of-the-art baselines by threefold in F1 score and by 23.2% in accuracy. Ablation studies validate the effectiveness of both the ACE and IDA modules. Tests on unseen network scales and real-world network topologies show the superior adaptability of our GSID, compared to the baselines.
Traditional network experiments focus on validation through either simulation or emulation. Each approach has its own advantages and limitations. In this work, we present a new tool for next-generation network experiments created through Artificial Intelligence (AI) coding agents. This tool facilitates hybrid network experimentation through simulation and emulation capabilities. The tool supports three main operation modes: pure simulation, pure emulation, and hybrid mode. AgenticNet provides a more flexible approach to creating experiments for cases that may require a combination of simulation and emulation. In addition, AgenticNet supports rapid development through AI agents. We experimentally evaluate the tool and present an approach to verify the generated code.
Quantum key distribution (QKD) is a method for distributing cryptographic keys between remote endpoints, enjoying security based on quantum physics. This paper makes a first step towards studying topologies for QKD networks. QKD networks imply several required characteristics for their reliability and efficiency. We express such properties in terms of graph structure. For the comparison of potential graphs, we describe cost functions that allow us to identify families of graphs that are reliable and efficient. To make the discussion realistic, we summarize representative field-reported QKD performance numbers and illustrate the sensitivity of secret-key rate (SKR) to a few dB of additional loss. Last, we present methods to construct large efficient graphs through connecting smaller graphs.
We consider the problem of learning models of spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain. Deriving such models can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of applicability of off-the-shelf mixture density network models and of, two varieties of, normalizing flows for the description of mobile node density over a disk. We introduce the use of Möbius distributions to retain symmetric spatial relations. Our results indicate that mixtures of Möbius distributions provide interpretable, parsimonious models for the studied steady state density distributions, that match or outperform the alternatives.
Wireless Access and Backhaul (WAB) is emerging as a key enabler for flexible and cost-efficient 5G deployments, offering a modular architecture that decouples access and backhaul while supporting multi-technology and mobile backhaul links. This article introduces the WAB framework standardized in 3GPP Release 19, outlining its architecture and operational principles. A practical implementation built with commercial hardware and open-source software demonstrates the feasibility and efficiency of WAB systems. We further explore four representative application scenarios - ranging from on-demand coverage to mobile Software-Defined Wide Area Network (SD-WAN) connectivity - and discuss the technical challenges that must be addressed for large-scale adoption. These insights highlight WAB as a promising foundation for 5G-Advanced and a stepping stone toward future 6G networks.
Reconfigurable Intelligent Surfaces (RISs) are an attractive technology for Millimeter Wave (mmWave) communications due to their ability to passively reflect incident signals. However, current implementations of RIS rely on performing computationally-intensive algorithms offline to generate phase masks, which are stored as a codebook on the embedded microcontroller on the RIS. The codebook size is restricted by the embedded microcontroller's storage capacity, which limits the ability of the RIS to adapt to evolving channel conditions and deployment scenarios. In this demo, we showcase an Machine Learning (ML)-based solution for dynamically generating new phase masks during runtime. Our approach leverages a ML model deployed on the microcontroller for approximating the output of a phase mask generation algorithm, responding to new inputs while remaining smaller than a codebook.
Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This work investigates the ability of Large Language Models (LLMs) to generate structurally valid and constraint-compliant network topologies through a constraint-driven pipeline combining hierarchical modeling and systematic validation. The framework is evaluated via a multimodel comparison of proprietary and open-weight LLMs across four realistic network scenarios released as a public dataset. We assess structural correctness using node and edge F1-scores against reference topologies, and evaluate resilience through server and content connectivity metrics. In addition, we analyze common failure modes, including interface mismatches and directional inconsistencies in generated topologies. Overall, this work provides a systematic benchmark for understanding how LLMs handle structural and resilience constraints in topology synthesis, and supports informed model selection for AI-driven network design.
Next-generation extended reality (XR) networks rely on mmWave communication for multi-gigabit throughput, yet highly directional links are vulnerable to user mobility and blockages, causing frequent outages under reactive beam management. Emerging neural radio fields can predict radio propagation, but prior work remains limited to offline channel reconstruction. We introduce RadioSight, a real-time multi-modal radio field system for predictive mmWave optimization and proactive Multi-User MIMO beamforming. RadioSight combines backward beam-tracing with real-time semantic object synchronization to anticipate RF geometry changes without full model retraining. Implemented as an edge-executable pipeline for commercial 28 GHz arrays, RadioSight determines each scheduling window's beams during the preceding window without exhaustive beam sweeps. Experiments show that RadioSight reduces beam-search error by up to ~50%, improves median throughput by 2x, and enhances link stability.
Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers, but the two tasks have different uncertainty characteristics. This letter proposes an uncertainty-aware multi-task model that transforms each short normalized in-phase/quadrature window into 36 deterministic, label-free statistics, learns a shared representation, and uses task-specific adapters for modulation classification and heteroscedastic SINR regression. A joint uncertainty score combines classification entropy and predicted regression variance to support selective inference. Simulations cover QPSK, 8PSK, 16QAM, and 64QAM under matched additive white Gaussian noise/Rayleigh channels and an unseen frequency-selective Rician channel. Over five independent seeds, the proposed model improves matched and unseen-channel accuracy over conventional multi-task learning by 14.86 and 8.61 percentage points, respectively, while reducing SINR mean absolute error by 1.60 and 1.61 dB. Confidence-based rejection further lowers modulation error under channel mismatch.
Wireless systems usually react to current channel conditions, queue state, and policy. Yet service conditions can often be anticipated seconds ahead. This paper studies predictive traffic shaping, a slower user-equipment (UE) control loop that changes when flexible demand reaches the radio access network (RAN). The UE estimates useful pre-event demand and releases it across a lookahead window. Cooperative deployments may also send a compact future-risk descriptor to the network. The trigger uses prediction confidence. Predictive service is confined to available surplus, while a debt account preserves long-term fairness after temporary pre-event preference. A bandwidth-time model captures efficiency gains and load smoothing. It also accounts for prediction waste and shared-resource cost. In a stylized shared- cell simulation, paced demand release substantially expands the stable-feasible region. A safe service cap and admission control provide further gains at longer windows. PRISM, an application-owned middleware prototype, implements the local control policy using ordinary mobile transfer mechanisms.
Path-aware networking (PAN) architectures, such as SCION and emerging LEO constellations, expose tens to hundreds of verifiable paths to endpoints. When multipath protocols like MPTCP and MPQUIC greedily exploit this diversity, uncoordinated migration can induce persistent, high-amplitude load oscillations. Although this instability is well-known, its quantitative performance impact remains poorly understood. In this paper, we apply a discrete-time axiomatic framework to the joint dynamics of loss-based congestion control and greedy path selection. By deriving the system's dynamic equilibria (stable periodic oscillations), we prove a fundamental trade-off: high Responsiveness improves Fairness but necessarily degrades Efficiency and Convergence. Conversely, we demonstrate that Efficiency, Convergence, and Loss Avoidance are simultaneously achievable at a critical lossless operating point. Furthermore, we find that while migration de-synchronizes traffic in high-diversity environments, realistic limited-visibility constraints transform coherent oscillations into persistent spatial load imbalance, rather than eliminating instability entirely. These results yield concrete design guidelines for robust multipath transport over the future path-aware Internet.
This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these temporal features. This study addresses this gap by creating and making publicly available a set of NetFlow datasets that incorporate these temporal features [1]. With these temporal features, we provide a comprehensive temporal analysis of NetFlow datasets by examining the distribution of various features over time and presenting time-series representations of NetFlow features. This temporal analysis has not been previously provided in the existing literature. We also borrowed an idea from signal processing, time frequency analysis, and tested it to see how different the time frequency signal presentations (TFSPs) are for various attacks. The results indicate that many attacks have unique patterns, which could help ML models to identify them more easily.
Classifying VPN-encrypted traffic by application category typically relies on datasets that collect non-VPN and VPN traffic in separate sessions, conflating encapsulation effects with session-level differences in user behavior, timing, and application mix. We use a recently published WireGuard tunnel dataset in which pre- and post-tunnel traffic is captured simultaneously, with a packet-level match ratio above 99.9%. This matched-capture design eliminates session-level confounds and enables a cross-domain benchmark: models are trained on non-VPN flows and tested on the VPN view of the same underlying flows. We compare whole-flow statistical aggregates (FlowFeatures) and Sequence of Packet Length and Time (SPLT) early-flow fingerprints across Random Forest, XGBoost, and a multi-scale CNN1D. Cross-domain transfer depends jointly on representation and model: tree ensembles achieve balanced accuracy of 0.84-0.93 with FlowFeatures but only 0.60-0.75 with flattened SPLT, whereas CNN1D processes the same SPLT fingerprint as a sequence and achieves the strongest transfer overall (balanced accuracy 0.98, macro F1 0.89) without any VPN data during training.
Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in the wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly dynamic, resource-constrained, topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these limitations, this paper proposes the memory-native NTN (Mem-NTN) paradigm that leverages long-horizon contexts for memory-augmented system optimization. To realize this paradigm shift, we establish a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience. We develop memory acquisition, compression, valuation, update, and utilization mechanisms that facilitate cross-layer, memory-native decision-making, spanning from the physical and access layers up to the network and application layers. Experiments in satellite embodied question answering (SEQA) demonstrate that the proposed Mem-NTN consistently outperforms conventional stateless NTN and terrestrial approaches.
Highly dynamic and mobile applications, such as vehicular networks, require stable connectivity, which is often challenging to achieve. Network densification is a key approach to address this issue and can be achieved cost-effectively through mobile base stations and wireless relaying. However, existing solutions rely on rigid and complex architectures that hinder deployment in dynamic scenarios. The recently standardized Wireless Access Backhaul (WAB) architecture represents a key evolution, enabling flexible and modular wireless relay networks with native support for mobility and multi-technology wireless backhaul. This paper presents the first experimental realization of a multi-band WAB testbed, combining an FR2 backhaul and an FR1 access link using open-source software and commercial off-the-shelf components. The proposed framework validates end-to-end WAB operation under mobility and demonstrates the extension of FR2 coverage while maintaining compatibility with legacy FR1 user equipment. Experimental campaigns in vehicular and outdoor-to-indoor scenarios confirm that WAB effectively mitigates FR2 limitations, particularly in uplink and Non-Line-of-Sight conditions. These results highlight WAB as a practical and scalable approach for vehicular and next-generation wireless networks.
This paper addresses multi-objective conflicts and catastrophic forgetting in uncrewed aerial vehicle (UAV) networks across dynamic spatiotemporal environments. Conventional multi-agent reinforcement learning (MARL) algorithms suffer from severe policy degradation during sequential task transitions. We propose a spatiotemporal continual federated learning (SCFL) framework driven by the group-decoupled multi-agent proximal policy optimization (G-MAPPO) algorithm. SCFL incorporates a three-stage geometric alignment mechanism: it resolves local gradient conflicts via group-decoupled policy optimization (GDPO), mitigates spatial Non-Independent and Identically Distributed (non-IID) client drift through adaptive cosine aggregation, and suppresses inter-task interference via global temporal orthogonal projection without raw experience replay. Evaluations show that SCFL achieves superior robustness over federated baselines, maintaining spatial service reliability above 0.95 and a load balancing index of approximately 0.95 during non-stationary transitions. A longitudinal self-degradation analysis further shows that SCFL preserves historical knowledge with near-zero performance variation in spatial reliability and QoS under moderate loads from 40 to 120 users, while revealing its operating boundary under extreme congestion with 140 users due to hard projection constraints. The framework provides a scalable, communication-efficient approach for autonomous aerial network orchestration.
Network slicing over space-division-multiplexed elastic optical networks (SDM-EONs) requires jointly managing spectrum, spatial cores, and compute resources, a coupling that many existing studies ignore by treating compute placement independently from routing and spectrum decisions. This disconnect can cause the spectrum to be allocated along a path, only for the request to fail due to insufficient compute resources along the path, or may result in compute resources being allocated without consideration for spectrum resource availability on the path between compute nodes. We propose a path-constrained reinforcement learning framework that addresses compute node selection and RMCSA, being aware of both resources, restricting the RL agent's action space to nodes along $k$-shortest paths between request endpoints. Training incorporates reward shaping to improve robustness under high load. We propose PPO-Full (Proximal Policy Optimization-Full), which jointly selects compute nodes and routing paths via a multi-dimensional action space, against distance-based heuristics, a greedy baseline, and a decoupled VONE-DRL baseline on a 24-node USNET topology under hotspot traffic conditions. Results demonstrate consistent improvements in acceptance rate over all baselines at high load, with gains becoming more pronounced as traffic intensity increases.