Papers

As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling technology for next-generation intelligent traffic management. However, fully realizing this potential is hindered by the limitations of current paradigms. Existing approaches typically optimize localized interactions rather than system-wide efficiency, incur severe communication overhead, or lack the deterministic guarantees required for safe kinematic execution. Furthermore, current multi-agent adaptations are frequently restricted to small predefined scenarios, failing to scale across large and complex urban networks. To bridge this gap, this paper introduces VeloCity, a decentralized multi-agent spatio-temporal mobility profile planning framework designed for CAVs operating in arbitrary urban areas. To minimize vehicles' travel times, VeloCity distributes mobility profile optimization directly to individual CAVs. Vehicles query a localized traffic coordinator for a reservation table, independently compute their fastest conflict-free mobility profile, and reserve their requested space-time slots back with the coordinator. By natively adapting to any arbitrary road topology, the framework manages highly irregular urban areas without requiring scenario-specific tuning, all while guaranteeing collision-free and physically executable vehicle trajectories. Extensive simulations across four large-scale real-world urban maps (Tokyo, Manhattan, Rome, and Bologna) demonstrate the framework's scalability. Compared to established state-of-the-art models, VeloCity yields drastically lower travel times, tightly bounds delay variance, and successfully prevents congestion gridlocks even under extremely high vehicular densities.

Future wireless networks are expected to support the coexistence of cellular communications, radio frequency (RF) sensing, radionavigation, and radiolocation radar-among others-over congested federal sub-6 GHz spectrum under heterogeneous Quality of Service (QoS) requirements, driven by escalating cellular traffic demand, the proliferation of 6G sensing and positioning services, and regulatory pressure to repurpose federal bands without displacing incumbents. We develop a unified framework in which all four services dynamically share a common Physical Resource Block (PRB) pool under centralized coordination, formulating weighted cellular sum-rate maximization subject to duty-cycle, orthogonality, sensing signal-to-noise ratio (SNR), and Cramer-Rao-based positioning constraints. The resulting Mixed-Integer Nonlinear Program (MINLP) is solved by alternating optimization across PRB assignment, scheduling, and successive convex approximation for power allocation, complemented by a low-complexity QoS-aware greedy heuristic. Site-specific ray-tracing simulations on the BostonTwin urban digital twin show substantial gains in spectrum efficiency and cellular throughput while strictly meeting sensing and positioning QoS, establishing coordinated multi-service sharing as a viable architecture for cellular-federal-radar coexistence in next-generation military and civilian networks.

We study how to share a single conserved capacity budget across many locations and two service classes when demand is uneven, time-varying, and can exceed supply. The shape recurs: an origin's request-rate cap split across its edge locations, a licensed throughput cap across premium and standard tenants, or an egress budget between latency-critical and batch workloads. We present a two-level algorithm. The first level redistributes capacity within a class across locations by proportional deficit and excess redistribution; the second lends capacity elastically between classes when one has surplus and the other deficit. We prove it conserves the budget exactly, preserves non-negativity, and reaches a stable allocation in one iteration under stationary demand because it carries no per-cycle state, at O(KN) cost per cycle for K classes and N locations. We evaluate it defending a CDN's per-domain budget under volumetric attack, where the classes are confirmed-legitimate and not-yet-cleared traffic; across 8 contention scenarios on a 22-location topology it serves 66-93% of high-priority demand, competitive with a single-class linear-programming optimum, while never leaving capacity idle or over-committing whenever aggregate demand meets or exceeds the budget (the contention regime these scenarios evaluate). Two findings carry beyond the application. First, a throughput-maximizing objective is wrong under contention: a two-class LP maximizing total served load serves less high-priority load than our demand-proportional, reservation-respecting allocator in most scenarios, because it cannot tell that some load it serves is the contention. Second, inter-class borrowing earns its complexity under bursty load, improving high-priority service by 1.5 points (isolated by ablation), and is neutral under stationary demand. A 5-location prototype with real HTTP traffic validates the pipeline.

Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the methods we compare require 3.3 to 9.8 hours per round, which makes round-based comparisons misleading. The problem is more obvious for same-scene multimodal clients, since camera, video, LiDAR, and radar workloads differ substantially in training and communication cost, while existing methods treat the modality composition of each round as fixed. To address it, we introduce FedSceneX, an orchestrator that jointly determines round composition to maximize learning value per active hour. The optimization method, Value-per-Hour Pricing (VHP), converts the fractional objective through a parametric transformation and dualizes the uplink constraint, yielding a closed-form client price whose weights capture resource shadow costs. Based on these prices, FedSceneX selects clients subject to a modality coverage constraint, allocates precision through reverse water filling, and assigns updates to edge servers. On the full nuScenes benchmark with fifteen clients and twelve baselines, FedSceneX reduces the active time per round to 3.31 hours, compared with 4.85 to 9.78 hours for the baselines. Across all random seeds, it achieves the highest accuracy within a twenty-hour budget while preserving all four modalities. Its advantage persists from ten to forty-five hours, after which conventional methods overtake it.

Phishing attacks have always been a favored vector for adversaries to defraud users, bypass modern defense mechanisms, and penetrate critical systems. Among all the elements contributing to the creation and deployment of successful phishing attacks, phishkits stand out as a crucial parameter. Phishkits often facilitate creating and deploying compelling phishing pages, implement evasion strategies, and establish and maintain backdoors with remote adversaries for exchanging leaked data. In this work, we performed an analysis of 1,300 modern phishkits collected from 2020 to 2023. We analyzed the architecture, source code, communication channels, and the nature of leaked data shared with adversaries. We identified mechanisms for dynamic redirection and attributing incoming web traffic as part of the evasion and cloaking mechanism. We also observed heavy reliance on current messaging services for exchanging stolen data with phishers. That said, our analysis shows that the number of phishkits with advanced functionalities is quite small. We identified 284 (21.8%) phishkits that did not use any form of evasion mechanism. We also observed that while there were differences in the implementation details of phishkits, the major components that keep phishing pages functional were very similar or even identical across kits. The level of code reuse and heavy reliance on known tricks to build pre-packaged phishing pages make a large number of cases predictable, which can potentially make the detection of these adversarial operations even easier at scale.

While deploying hierarchical vision models to process mission-critical tasks, UAV edge systems must adaptively update the models to sustain inference reliability under low-level environmental corruption. However, existing work has overlooked the optimal timing for model updates, the impracticality of relying on real-time expert labels, and the significant bandwidth and energy constraints of UAVs. This paper proposes a joint model update scheduling and resource allocation framework, aiming to maximize long-term semantic fidelity and resource efficiency of UAV edge intelligence systems. To address the challenge of label-free semantic evaluation, we formulate the Online Semantic Disagreement Rate (OSDR) as a proxy for timely update triggering, thereby enabling fine-grained Sensitivity-Aware Structural Synchronization (SASS). Furthermore, to overcome the curse of dimensionality in hybrid action spaces and effectively bound long-term energy budgets, we propose a Lyapunov-guided discrete reinforcement learning algorithm that performs action space dimensionality reduction and transforms constraints into virtual queue stability problems. The reported experimental results, based on real traffic traces, demonstrate that the proposed framework consistently outperforms representative baselines in semantic recovery efficiency and update triggering precision, by satisfying long-term energy budget and by reducing average risk backlog by up to 33.3% in the dynamic environmental corruption scenario.

Quantum network links must distribute entanglement at high rates while satisfying application-specified fidelity demands. However, wide-area deployed fiber links suffer from polarization drift which destabilizes end-to-end fidelity and forces periodic compensation. Current deployments often use active stabilization with fixed control policies, and improvements generally stem from advances in quantum hardware. Meanwhile, software control remains relatively underexplored. Here, we formulate quantum link operation as a joint control problem over tunable rate-fidelity tradeoffs and uncontrollable link drift. From this framework, we construct a link control protocol that dynamically adapts source pump power and polarization compensation to maximize entanglement distribution rate subject to a minimum fidelity constraint. We evaluate the protocol through trace-driven simulations driven by data from a 64 km deployed optical fiber. Compared with optimized static policies, our adaptive controller improves mean entanglement distribution rate by 14% over a 24 hour trace, without requiring any offline policy optimization. Our results show that software-based physical layer control can provide a practical mechanism for improving near-term quantum link performance without requiring additional quantum hardware.

Despite recent advances in applying artificial intelligence (AI) techniques to radio access network (RAN), critical challenges remain: traditional machine learning (ML) algorithms suffer from limited generalization across varying network topologies, whereas general-purpose large language models (LLMs) face high computational demands and lack domain-specific knowledge. To address these gaps, this article introduces the evolving RAN intelligent controller (RIC) (EvoRIC) framework, a hierarchical architecture that enables continuous evolution by leveraging a non-real-time RIC (non-RT RIC) for global model updates and a near-real-time RIC (near-RT RIC) for local execution, dynamically empowering LLMs with domain-specific decision-making capabilities. Within this framework, we employ a reinforcement learning-based fine-tuning (RLFT) mechanism where an LLM operates as an actor within a proximal policy optimization (PPO) agent. By leveraging the interaction tuples collected from the wireless environment, the LLM's parameters are iteratively updated to align semantic reasoning with rigorous network performance objectives. We evaluate the generalization and efficacy of the proposed EvoRIC framework within integrated access and backhaul (IAB) networks, and finally, discuss the open challenges and future directions of the EvoRIC framework toward realizing autonomous O-RAN.

Existing Greedy Perimeter Stateless Routing (GPSR)-based protocols for Flying Ad-Hoc Networks (FANETs) struggle to adapt routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, under highly dynamic environments. As an emerging artificial intelligence technology, large language models (LLMs) show potential for intelligent decision-making, providing new opportunities for adaptive adjustment of GPSR parameters to improve network performance. However, applying LLMs to GPSR remains challenging due to irrelevant experience retrieval and the absence of protocol constraints. To address these issues, we propose a Parameter-Specific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKR-GPSR), an LLM-based framework that enables protocol-consistent routing parameter adaptation. We design a parameter-specific multi-index retrieval mechanism to provide LLMs with parameter-relevant experiences while reducing interference from irrelevant information. We further construct a knowledge-guided constraint graph to enforce that the routing parameters satisfy dependency rules and optimization constraints. Simulation results demonstrate that PMKR-GPSR achieves higher packet delivery ratio and lower end-to-end delay under high-mobility FANETs.

The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.

The significant energy consumed by data centers has become a concern both for costs and associated carbon emissions. In particular, the energy efficiency of servers is a key consideration for data center operators, and understanding servers' power consumption under different operating conditions is an important aspect of it. In this paper, we present a measurement-based case study of high-throughput computing. We analyze power usage information of an operational data center, combined with focused measurements of power reduction techniques for a representative high-throughput workload. The study points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and discusses the impact of server configuration adjustments on the energy consumption of processing jobs. We offer actionable recommendations for decreasing the energy usage of servers, while considering both performance and carbon emissions.

IP addresses are commonly shared across devices and users for a variety of reasons, including NAT and proxies. These technologies operate at different scales, from residential NATs that share an IP address across devices in a home to large-scale Carrier Grade NATs that share hundreds or thousands of users on a single IP. Cases of large-scale IP sharing are distinct as they have significant implications for IP-based mechanisms such as attribution, blocklisting, and rate-limiting, where the consequences of mishandling affect a large quantity of end-users and organizations. In this work, we detect and characterize IP addresses shared at large scales, which we coin massively shared. Leveraging diurnal patterns in traffic shape, we use data from a large CDN to characterize these IPs globally. We broadly find that massive IP sharing is responsible for a large fraction of IPv4 traffic, concentrated in a small fraction of address space, with over 40% of total traffic coming from less than 2% of active IP addresses. We observe distinct patterns in deployment geographically, with particularly high rates of massively shared traffic from some smaller countries. Comparatively, in IPv6, we find far fewer massively shared addresses with some surprising exceptions among mobile providers. We additionally contextualize these addresses by other network characteristics, including identifying cellular connectivity and dual-stack capabilities, and identifying several instances of massively shared IPs in proxy services hosted on cloud networks. Finally, we find that rates of massively shared traffic are increasing over time, predicting future reliance on these technologies. Our work contextualizes the state of IP sharing, providing a uniquely broad perspective globally.

In modern distributed network environments, particularly in Internet of Things infrastructures and 5G networks, stringent privacy preservation and scalability requirements have created significant challenges for intrusion detection systems. Although federated learning preserves privacy by preventing data centralization, its efficiency and stability is considerably degraded under severe statistical heterogeneity and resource constraints of edge nodes. To address these limitations, this study introduces the FedTransKD-IDS framework, which enhances both system stability and efficiency by integrating robust aggregation based on the geometric mean, federated transfer learning, and knowledge distillation. Within this framework, the collaboratively trained global teacher model transfers its feature extraction component to lightweight student models. Experimental evaluation on heterogeneous datasets demonstrates a peak detection performance, achieving an accuracy of 99. 18% and a recall of 99. 99%, thereby indicating the effectiveness of structured knowledge transfer in federated environments.

Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws. This stems from their inability to maintain reliable world models for long-horizon planning under uncertainty and generalize to unseen scenarios. In this context, wireless networks, through pervasive sensing and communication, can orchestrate physical intelligence. However, current architectures optimize throughput, latency, and reliability and cannot support real-time physical AI coordination, requiring agents to maintain shared spatiotemporal context. To address these challenges, a network of holonic digital twins (HDT-Nets) framework is proposed to deliver real-time physical AI inference through holonic agents that actively reason about their environment rather than passively mirror physical assets. Each HDT is realized as a hierarchical structure spanning the physical agent and network edge, reasoning autonomously at the local level while cooperating with neighboring HDTs to form collectively intelligent units. In HDT-Net, causal Markov blankets spanning sensing, communication, and control determine which agents must coordinate and enable counterfactual reasoning over multi-domain interventions. Active inference within these boundaries unifies perception, action, and learning by minimizing expected free energy while deciding which beliefs to transmit based on their cognitive value to the receiver. Category theory ensures that transmitted beliefs preserve semantic structure across heterogeneous agents with incompatible representations. Finally, integrated information theory quantifies when collective intelligence exceeds independent operation and how network intelligence evolves through coordinated learning and information exchange.

Live immersive video streaming, particularly 360-degree video, is increasingly adopted in applications such as virtual events, sports broadcasting, and remote education. Existing approaches struggle to support high-bitrate immersive streaming for large numbers of concurrent users, with coarse-grained delivery limiting responsiveness and insufficient support for coordinating concurrent tile streams. Media over QUIC (MoQ) has recently emerged as a promising solution for large-scale media delivery, yet it lacks robustness under bandwidth-constrained conditions, often resulting in playback stalls. To address these challenges, we present MultiMoQ, a multi-access tile streaming framework built on MoQ that redesigns its delivery mechanism for robust high-bitrate streaming across multiple access paths while supporting flexible tile scheduling and seamless access switching. We implement a fully functional prototype of MultiMoQ and evaluate it in network emulation under heterogeneous real-world network conditions, comparing against Dynamic Adaptive Streaming over HTTP (DASH) and standard MoQ. Results show that MultiMoQ increases goodput for enhancement tiles and base video and reduces enhancement-tile tail end-to-end latency relative to DASH, while preserving audio continuity and avoiding the persistent stalls of standard MoQ. These transport gains translate into smoother viewport playback, and the ablation results further confirm the contribution of multi-access control to playback continuity.

Multipath transport is important for Internet/WAN services that move data volumes across heterogeneous paths, including geo-distributed analytics, content distribution, and cloud-service pipelines. Existing solutions face a trade-off: end-to-end transports such as MPTCP and MPQUIC are deployable but limited by endpoint-visible paths and delayed congestion feedback, while routing- or forwarder-assisted approaches often require infrastructure support or lack safe coordination across forwarding choices. This paper presents MARS, a receiver-driven, forwarder-assisted multipath transport. MARS combines tier-synchronized overlay path discovery with coupled consumer/forwarder congestion control, enabling it to expand usable forwarding opportunities and react near bottlenecks. It runs as an incrementally deployable UDP overlay at clients, servers, relays, or CDN-like nodes. We implement MARS in simulation and as a prototype, and evaluate it through simulation and Mininet emulation across deployment scopes, loss rates, and a forwarding-face outage scenario. Results show MARS provides deployment-dependent benefits: with endpoint-only deployment, it performs comparably to the evaluated ECMP-limited configurations of MPTCP and MPQUIC. With cooperating overlay forwarders, it expands the usable path set from routing-exposed forwarding candidates. Across the tested loss conditions, it reduces maximum T95 by up to 66.7% and 63.9% relative to the evaluated path-expanded MPTCP and MPQUIC configurations, respectively, given the same path set. Path discovery remains lightweight, flow fairness remains high, and MARS degrades gracefully during an emulated forwarding-face outage and recovers quickly after face restoration. Overall, ICN-style receiver-driven forwarding can serve as a deployable overlay transport substrate for coordinated WAN multipath without requiring changes to IP routing.

AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, training and fine-tuning jobs compete with co-running workloads for network resources, while network mechanisms and ML training choices are typically optimized separately: networking controls how bytes move, whereas ML systems control when and how much communication occurs. We argue that this separation leaves end-to-end performance on the table. We present ML-for-ML, a cross-layer perspective in which network-side and ML-side knobs are selected jointly under a shared time-to-target-loss objective. Our preliminary prototype shows that by co-optimizing the ML and network parameters, we reach the target loss up to 42% faster.

Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs. Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneous demands under limited edge computing resources. To address this challenge, we propose a hybrid autoregressive-speculative inference (BALANCE) framework for edge LLM inference. In BALANCE, an edge server hosts both an SLM and an LLM, admits users, assigns each admitted user to the AD or SD mode, and performs the two modes simultaneously. To maximize the number of served users, we formulate a task throughput maximization problem to jointly determine user admission and computing resource allocation between AD and SD under user latency requirements and server memory constraints. Since the problem is NP-hard, we develop a polynomial-time algorithm that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee. Experiments demonstrate that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.

Integrated Sensing and Communication (ISAC) lets cellular infrastructure serve communication users and sense on the same waveform. We present an end-to-end O-RAN simulation testbed for 5G ISAC targeting low-altitude UAV detection and 3-D tracking, built from open-source components: OpenAirInterface, FlexRIC and Sionna RT, in which the NR Uplink Sounding Reference Signal is repurposed as a passive radar waveform: a PHY-layer sensing stage inside the gNB produces detections that reach an Extended Kalman Filter tracking xApp over a custom E2 service model, with no change to the NR standard and no dedicated sensing waveform. A single bistatic pair leaves elevation unobservable, so the tracker needs a height prior; we remove it two independent ways and measure both - a planar receive array supplying a vertical aperture, and a second transmitter supplying range diversity. Both live results corroborate an offline ray-traced study of the same estimator, which converges from a deliberately wrong initial altitude to 1.8 m RMSE at a consistent filter, so altitude observability is established both in the signal-processing chain and end to end through the live stack. Detection coverage is preserved under a concurrent 10 Mbps uplink communications load.

Time-varying cloud workloads often cause resource under-utilization during off-peak periods and resource contention during peak periods. Existing prediction-then-optimization (PTO) frameworks suffer from two-stage decoupling, hindering the balance among violation rate, user satisfaction, and resource utilization. We formulate heterogeneous job scheduling as a multi-objective combinatorial optimization problem (MOCOP) under uncertain constraints and propose a closed-loop decision-focused learning (CL-DFL) framework for cloud orchestration. CL-DFL integrates a Multivariate Time-series Graph Neural Network (MTGNN)-based spatio-temporal predictor with a zeroth-order decision-focused learning (DFL) mechanism based on the tree-structured Parzen estimator (TPE). This integration establishes an end-to-end (E2E) feedback pathway between resource perception and scheduling decisions. Furthermore, we develop the GNeuro-PLS strategy by incorporating group relative policy optimization (GRPO) into cooperative local search to improve robustness under heterogeneous workloads. Extensive experiments on four real-world datasets demonstrate that CL-DFL achieves superior trade-offs among violation rate, user satisfaction, and resource utilization. It effectively controls overload risks under regular workloads and maintains resilience under highly saturated scenarios compared with state-of-the-art baselines.

Ultra-Reliable Low-Latency Communication (URLLC) requires strict reliability and latency guarantees for heterogeneous periodic traffic. Proactive HARQ improves resource efficiency through early termination, but slot-level timing effects, particularly delayed feedback, complicate schedulability analysis. This paper presents a discrete-time Markov chain (DTMC)-based framework for periodic flows with proactive HARQ. By expanding the state space, the model captures HARQ round-trip time and other cross-slot timing effects. The framework determines the transmission opportunities required to satisfy heterogeneous reliability and latency constraints and supports offset-based scheduling through a two-stage genetic algorithm. Simulations with industrial URLLC traffic show that the proposed method achieves higher schedulability than reactive HARQ, K-Repetition, and non-guaranteed proactive HARQ, with acceptable computational overhead.

Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their normal traffic patterns which are characterized by massive, bursty "elephant flows" are statistically indistinguishable from volumetric attacks such as DDoS to conventional monitoring systems. This similarity leads to high false-positive rates in anomaly detection, blinding security operators to genuine threats. In this paper, we propose and evaluate a high-fidelity traffic forecasting framework designed to establish dynamic security baselines for RENs. Leveraging an exclusive 57-day Internet2 dataset spanning ten backbone routers (13.7 billion packets), we perform the first large-scale benchmark of anomaly-aware forecasting models in this domain. We systematically evaluate six model families, from SARIMA to state-of-the-art long-sequence architectures (TiDE, PatchTST), across 960 experimental configurations. Our results demonstrate that these advanced architectures, particularly TiDE, reduce baseline prediction error by 30-42% compared to traditional methods ($p < 0.001$), significantly improving the distinction between legitimate scientific bursts and potential anomalies. Furthermore, we introduce a novel anomaly-integration strategy that improves model robustness by 3.3% in the presence of noise. This work provides the first statistically validated framework for distinguishing scientific workflows from network attacks, enabling more autonomous and resilient network security operations.

Future 6G networks must manage increasingly dynamic radio environments in which multiple autonomous sub-networks (SNs) share frequency resources and adapt to changing operating conditions. In such scenarios, interference from faulty devices or intentional jamming can disrupt ongoing communications, making rapid and autonomous network adaptation essential. This demonstration presents a self-healing networks-in-network (NiN) architecture that closely integrates the detection of spectrum anomalies with dynamic spectrum management. A spectrum scanner continuously monitors the frequency spectrum and forwards detected anomalies to the DSM, which automatically identifies suitable frequency resources and reconfigures the affected SN. During the live demonstration, participants can initiate controlled disruptions and observe the entire adaptation process in real time, from anomaly detection to autonomous frequency reallocation and network recovery. The demonstrator illustrates how integrating spectrum monitoring and resource management into a single control loop can improve the resilience of future NiN implementations and demonstrates a practical approach to autonomous, spectrum-aware networking.

Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR). However, existing TSN scheduling solutions predominantly rely on static optimization techniques or centralized learning models that are based on fixed traffic patterns, limiting their effectiveness in dynamic environments. In practice, MEC environments often host multiple co-located XR traffic flows whose characteristics evolve over time, creating complex inter-queue dependencies that current schedulers fail to capture. Addressing these challenges requires adaptive, decentralized scheduling mechanisms capable of coordinating multiple TSN queues under varying traffic conditions. To this end, this paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent. The Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery across queues. The simulation results demonstrate that the proposed approach reduces average frame waiting times by up to 26.8% and worst-case delays by approximately 16.8%, highlighting its effectiveness in dynamic XR-driven MEC scenarios.

Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR). However, the widespread adoption of XR introduces significant challenges due to co-located services in MEC environments, leading to contention for shared network resources. Moreover, XR traffic types have distinct characteristics and criticality in terms of timing requirements, further increasing the complexity and dynamics of such environments. Although reinforcement learning has shown promise for TSN scheduling optimization in dynamic network scenarios, existing approaches rely on centralized or high-level multi-agent designs and are typically tailored to periodic and predictable industrial traffic, limiting their applicability to XR workloads. As a result, these approaches suffer from (i) limited ability to capture inter-queue dependencies due to coarse-grained control, and (ii) poor adaptability to highly dynamic and heterogeneous XR traffic. To address these gaps, we propose a multi-agent reinforcement learning approach for queue-level XR traffic scheduling. We adopt the multi-agent transformer (MAT) to model inter-queue dependencies via attention over agents' observations and actions, enabling implicit coordination across heterogeneous co-located XR applications. Our simulation results show that the proposed method outperforms baselines, achieving up to 71.42% latency reduction and up to 83.2% reduction in failure rate, while consistently achieving high reliability across all queues.

We study the problem of learning compact rule-based compressors for structured network traffic. Each packet is a record of header fields that are highly redundant within a flow, and a compressor is a small set of rules matching such records and replacing predictable fields with short codes. We cast rule learning as a two-stage problem: (i) an unsupervised structure-discovery stage that recursively partitions training packets using a normalized entropy-ratio criterion robust to small samples, and (ii) a constrained selection stage that uses dynamic programming to pick the rule subset maximizing expected compression gain under a hard budget on the number of installable rules. We instantiate the framework on Static Context Header Compression (SCHC), the IETF standard for rule-based header compression in constrained networks, and evaluate it on four real-world Internet-of-Things and 5G core-network datasets. Our method, Robust Entropy Clustering for Adaptive comPression (RECAP), surpasses expert-engineered rule sets with a small number of learned rules and removes the need for manual rule design.

Domain Name System (DNS) protocol analysis is fundamental to understanding and fortifying the Internet naming infrastructure. However, the lack of automated, portable, and user-friendly environment orchestration tools imposes significant overhead on researchers and severely limits the reproducibility of DNS studies. In this paper, we present Dart, an automated toolkit specifically engineered for DNS protocol analysis. Dart employs a declarative syntax to abstract the intricate software dependencies and heterogeneous configuration requirements of diverse DNS implementations, providing a unified and streamlined orchestration interface. We describe the architecture of Dart and evaluate its performance. Furthermore, we present two case studies that illustrate how Dart enables researchers to construct portable DNS analysis environments with a single command. To foster transparency and facilitate future research, all replicated environments and configurations will be open-sourced.

Migrating microservices across virtual machines (VMs) without knowledge of their runtime communication patterns risks creating cross-VM hotspots and latency spikes that are difficult to predict from static analysis alone. We use extended Berkeley Packet Filter (eBPF) kernel-level network tracing to automatically discover inter-service dependencies at runtime, with no application instrumentation, and use the resulting dependency graph to produce a traffic-aware migration plan ranked by return on investment (ROI). A two-pass process-identifier (PID) to port correlation algorithm recovers the identity of all 20 services in a shared-runtime testbed where processes are otherwise indistinguishable, matching the known ground-truth topology. The system discovers 32 dependency edges from 13,615 network events captured in three minutes, and applies spectral graph clustering with Kernighan-Lin refinement to partition services into VM-coherent groups. In simulation over the discovered graph, our ROI-ranked migration order reduces cumulative cross-VM traffic exposure during the migration window by 27% relative to alphabetical ordering, a deterministic proxy for arbitrary dependency-blind ordering. Collection overhead is mixed: in a controlled A/B test at near-saturation load on a host with two virtual CPUs (vCPUs), throughput fell by only 4.4%, but median (p50) latency rose by 383% and 99th-percentile (p99) latency by 1,050%. We therefore recommend running captures off-peak or on dedicated sampling nodes rather than under production saturation. All results are from a single 20-service testbed that we authored; we make no claim about behavior on production dependency graphs.

This paper describes DeepSig's CUDA-based acceleration backend for the OCUDU physical layer and O-RAN fronthaul path, integrated through acceleration interfaces that are largely independent of the underlying acceleration mechanism. The design accelerates PDSCH, PUSCH, SRS, PRACH, split-8 lower-PHY transforms, and O-RAN fronthaul (O-FH) IQ compression/decompression while preserving existing factories, resource-grid interfaces, PRACH-buffer interfaces, and channel processors. CUDA-visible grids, device-side softbit buffers, stream events, pinned staging buffers, and managed-memory policies keep data resident on the accelerator when the platform and radio split permit it. On an NVIDIA DGX Spark platform with a GB10 GPU and ARM CPU host, representative measurements with CPU baselines pinned to high-capacity cores show up to 10.3x PUSCH speedup, 2.7x PDSCH speedup, 19.7x split-8 low-PHY RX speedup with slot-shaped batching and scattered mapped zero-copy, 91.4x O-FH BFP12 decompression speedup, and 28.8x PRACH detector speedup against the production CPU path, with CPU and GPU 10% BLER thresholds agreeing to within 0.064 dB in the tested PUSCH sweeps. The same resident pipeline provides an execution substrate for AI-RAN, allowing machine-learned channel estimation, neural receivers, and AI-native air-interface research to run beside standards-compliant baseband kernels.

LEO satellite networks are an attractive solution to support farm automation in Agriculture 4.0 because of their ubiquitous coverage. However, LEO networks often suffer from high latency volatility, which can limit their utility in mission-critical farm operations such as remote control. This paper studies highly-reliable remote control over LEO networks by (i) casting highly-reliable remote control as a quantile forecasting problem and (ii) proposing a high-quantile estimator that can predict latency spikes at a given reliability level. Our results, drawn from a real-world OneWeb dataset collected in a major agricultural hub in the US, show that the proposed estimator can support highly-reliable remote control on the farm by meeting reliability requirements while allowing the remote-controlled vehicle to operate at speeds up to 138.6% higher than what would be possible otherwise.