Papers

Multimodal decentralized learning enables collaboration across heterogeneous edge devices without sharing raw data, but differing modality sets create coupled computation and communication bottlenecks. Multimodal agents support cross-modal alignment while training more model components and sharing bandwidth across multiple modality overlays. Under bandwidth limits, these agents can become hot spots: transfers miss aggregation windows, and their updates become stale or underrepresented, degrading worst-agent accuracy. Asynchrony removes global blocking but does not by itself coordinate local cross-modal interaction with timely peer-update exchange. We present MODAIC (Modality-Decoupled Asynchrony with Interleaved Coordination), a wait-free system that coordinates local modality progress, peer-to-peer update delivery, and aggregation under heterogeneous compute and bandwidth. Hierarchical asynchrony lets modalities progress independently while enforcing reciprocal feature exchange; load-aware update fetching overlaps training with communication, limits concurrent serving load, and reuses late arrivals; and consensus-aware peer selection uses lightweight metadata to account for update availability, peer participation, and loss similarity without active probing. We implement MODAIC as a containerized multi-agent framework and evaluate it on local and cross-region cloud testbeds. Across AVE and KU-HAR, MODAIC improves average accuracy and multimodal worst-agent performance. On AVE at 200 Mbps per agent, it improves multimodal worst-agent accuracy by 11.0 percentage points at 1000 seconds over the strongest baseline. On the cloud testbed, it reduces time-to-accuracy by up to 30%. Ablations show that simpler load-balancing and delayed-aggregation variants leave a worst-agent accuracy gap, and stress tests show tolerance to WAN variability and partial tracker unavailability.

In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets because their parameterizations do not specify how posterior location, uncertainty and shape must change as observations accumulate. We propose center-estimated NPE (CE-NPE), which combines a statistical center with an explicit contraction rate and learns a local center correction and residual densities in posterior-scale coordinates. The center is a statistical estimate, such as a closed-form statistic or a simulator-based moment match, which does not require an evaluable likelihood. Our analysis explains how contraction amplifies center error and decomposes Gaussian posterior error into shape, mean and covariance contributions. On six regular benchmarks, CE-NPE gives the best reference-posterior agreement of all tested amortized estimators at every out-of-range size, up to five times the largest training size, without retraining; it also fits better within the training range on most of them. Matched controls and nonregular examples characterize the scope of these gains and identify the conditions on which they depend: an appropriate contraction rate, an accurate center, and a stable residual representation.

Terrestrial network (TN) failures during disasters can overload surviving base stations (BSs) and disrupt emergency services. A non-terrestrial network (NTN) provides fallback, but its feeder link can become a bottleneck as users migrate. This paper studies post-disaster network resilience through service-priority-aware orchestration of constrained NTN feeder capacity in a post-failure snapshot. A lightweight Cross-Entropy Method (CEM) tunes feeder, offload, and user-ranking parameters offline, not as an online artificial intelligence (AI) controller. A Python system-level simulator sweeps operating regions under abstract TN-NTN link and queueing models. Gains depend on failure severity. At a 180-Mbps feeder under total TN outage, the proposed policy increases the absolute emergency-weighted continuity metric Cemg from 0.399 to 0.666 relative to the matched rule baseline with uniform feeder scaling (paired gain: 0.267; 95% bootstrap CI: [0.264, 0.271]); mild damage can reduce aggregate continuity. Offline CEM feeder-weight tuning further improves severe-failure continuity over fixed service priorities, whereas tuned offload and user ranking provide no detectable incremental gain.

Network simulation plays an important role in HPC/AI system research by enabling exploration of network architectures, communication strategies, and system configurations at scales that may be impractical to study experimentally. Existing approaches to network simulator calibration and validation lack a principled methodology that combines parameter alignment, constrained inference, and independent validation for collective-centric HPC/AI networks. We present CALIPER, a methodology for systematic calibration and validation of HPC/AI network simulators. CALIPER distinguishes directly observable system parameters from auxiliary parameters which are configured via calibration and constrains the latter to feasible domains consistent with the target system. We evaluate CALIPER's hierarchical, scale-aware calibration and its effects on parameter identifiability and generalization using collective communication benchmarks on a reference system with up to 64-nodes at the Jülich Supercomputing Centre. Validation results show that the calibrated parameter configurations identified by CALIPER predict system performance with less than 5% mean errors across scale-up and scale-out network domains. CALIPER demonstrates that hierarchical, constrained calibration can produce simulator configurations whose predictive accuracy persists beyond the observations used for fitting.

Island-ready 5G deployments provide edge connectivity for isolated regions in crisis scenarios by switching to island operation when they are cut off from the global Internet. This work addresses open research challenges that hinder the practical realization of island readiness, with a focus on the required state synchronization and connectivity monitoring functionality. As a proof of concept and for performance evaluation, we integrate our solutions in open-source projects and emulate a country-wide 5G communication network with real-world cellular traffic. Our evaluation studies the switching latency of transitions to island operation, showing that most of the transition delay stems from the access network reconfiguration. We study the synchronization overhead of a highly distributed core network, finding that the synchronization traffic grows faster than linearly with the number of 5G cores, reaching peak synchronization data rates of 9.36MB/s for a deployment of 50 cores.

Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous states and actions while balancing near- and long-term consequences. Existing solutions generalize poorly across network tasks, require substantial task-specific redesign, and lack reliable evaluation of long- horizon action consequences. Methods that learn through online interaction additionally face unstable optimization. We present NetRL, a two-stage LLM-based reinforcement learning framework that combines supervised fine-tuning on demonstration trajectories with online policy optimization. A universal mixed-embedding interface combines textual task descriptions with precise numerical states, while a network action head produces valid decisions. At its core, NetGRPO evaluates candidate actions over multiple future trajectories, directs deeper search toward uncertain alternatives through confidence-aware budgeting, and performs group-relative, clipped, and KL-regularized updates. NetRL achieves 23.6% higher QoE than NetLLM, 12.2% lower job completion time than Genet, and 24.1% higher network utility than Orca, yielding state-of-the-art performance among the evaluated methods. Confidence-aware budgeting reduces end-to-end training time by 27.1% without reducing validation QoE. NetRL also converges stably and generalizes to unseen network traces without retraining. Code and model weights are available at the repository linked in this paper.

Large-scale distributed training increasingly makes collective communication bandwidth-bound. In modern GPU clusters, communication libraries such as NCCL implement collectives including AllReduce over heterogeneous interconnects such as NVLink, InfiniBand, and Ethernet, making efficient use of available network bandwidth a central systems challenge. ForestColl showed that collective communication on such fabrics can be formulated using fractional tree packing; its scalable polynomial-time construction is developed for AllGather, with AllReduce obtained by composing AllGather and ReduceScatter. We present DualColl, which targets bandwidth-bound AllReduce directly. Under the tree-based AllReduce model, DualColl applies a Garg–Könemann-style multiplicative-weights algorithm and, for any $0<\epsilon<1$, computes a feasible schedule whose throughput is at least $(1-\epsilon)^2$ of the optimal AllReduce throughput. Its key component is an exact minimum-cost collective-tree oracle that projects physical-link dual prices onto a logical graph of participating ranks, reducing each iteration to shortest-path computations followed by a minimum spanning tree. We further extend the framework to local equal-next-hop packet-sprayed routing and to networks without explicit per-tree bandwidth control. For the latter, we introduce a tree-coupled max-min bandwidth-sharing model and show that tree duplication approximates an explicit fractional allocation with a provable accuracy-complexity tradeoff. Finally, we extend the fractional-packing framework to additional collective operations and multiple concurrent collective groups.

Immersive extended reality (XR) applications are key enablers of human-centric connectivity in next-generation networks. However, meeting their high-throughput, ultra-low-latency, and high-reliability demands while preventing cyber-sickness remains a critical challenge, especially in environments with non-deterministic wireless connectivity. Fiber-To-The-Room (FTTR), extending widely deployed Fiber-To-The-Home (FTTH) infrastructure, offers a promising solution. Yet, satisfying the stringent Quality-of-Experience (QoE) requirements of XR collaborators demands robust resource allocation across the multi-tier cascade of FTTH, FTTR, and WiFi segments. In this paper, we propose a coarse-grained traffic forecasting-empowered proactive resource allocation and intelligent in-premise roaming framework. Here, artificial intelligence (AI) model complexities are scaled to the computational capabilities of each network segment. At the FTTR segment, we employ a hybrid temporal convolutional and bi-directional long short-term memory (TCN-BiLSTM) network for multi-modal XR traffic forecasting, integrating passive optical network (PON) and WiFi access points with multi-link operation (MLO) and orthogonal frequency-division multiple access (OFDMA). The resulting long-horizon forecasts reduce inference frequency, enabling coordinated FTTR-WiFi resource allocation and seamless network-orchestrated mobility across rooms. At the FTTH segment, we deploy Transformer-based time-series models (e.g., Informer, Autoformer, PatchTST, and TFT) to predict the complex XR traffic aggregated in FTTR segments for transmission over the FTTH backhaul. Evaluations show Transformer-based models are up to $\sim$75% more accurate in long-horizon forecasting than the hybrid model. Extensive simulations confirm the proposed framework significantly outperforms traditional methods in satisfying XR QoE requirements.

The telecommunications industry is shifting toward an experience-driven era defined by immersive applications that impose stringent network demands. While passive optical network (PON)-based Fiber-To-The-Home (FTTH) architectures have historically delivered high-capacity broadband, a growing performance gap between access and indoor networks necessitates driving fiber deeper into premises. Complementing existing copper and WiFi technologies, Fiber-to-the-Room (FTTR) is emerging as a critical enabler. This article examines the drivers of this architectural shift, specifically focusing on collaborative extended reality (XR). We analyze the multi-modal traffic characteristics and Quality-of-Experience (QoE) requirements of XR, alongside the resulting network performance implications. To address these demands, we propose a novel resource allocation framework integrating coarse-grained traffic forecasting with intelligent in-premise roaming. This framework facilitates the flexible deployment of artificial intelligence models tailored to the computational capabilities of the FTTH, FTTR, and WiFi segments. Numerical evaluations of a representative multi-tier network demonstrate that long-horizon, multi-step-ahead traffic forecasts significantly reduce inference frequency. This approach enables coordinated resource allocation and network-orchestrated seamless mobility across rooms, effectively satisfying collaborative XR QoE requirements. Finally, we highlight strategies to amortize FTTR capital expenditure by extending the optical infrastructure's role beyond basic connectivity. By supporting diverse services over a unified physical layer, FTTR enhances infrastructure utilization, unlocks novel revenue streams, and maximizes the value of in-premise fiber deployments.

Open Radio Access Network (Open RAN) enables multi-vendor deployments where concurrent RAN control applications (xApps) independently optimise mobility, load, coverage, and energy within the Near Real Time RAN Intelligent Controller (Near-RT RIC). When xApps act on overlapping control parameters, their isolated loops produce conflicting decisions that degrade the network Key Performance Indicators (KPIs). Existing conflict-evaluation methods estimate severity but do not answer whether the intervention is cost-justified. This paper proposes a cost-aware conflict triage framework that pairs ECDF-based severity evaluation with an event-level decision layer. The framework groups detected conflict records into episodes, extracts nine features, estimates per-mechanism costs and benefits, and assigns one of three actions: tolerate, defer, or mitigate. Evaluated in a simulator-driven Open RAN setting over 30 Monte Carlo runs with four concurrent xApps, the framework defers 66.8 %, mitigates 19.8 %, and tolerates 13.4 % of detected episodes, achieving a mean net utility of 0.132 per intervention, 3.9x higher than an always-mitigate baseline, by avoiding low- value interventions through cost-aware selectivity.

Response-supervision timers in the 5G Non-Access Stratum (NAS) bound the time allowed for signaling exchanges before retransmission or abnormal-case handling begins. In non-terrestrial networks, that interval can include satellite propagation, terrestrial transport, and processing by multiple core-network functions. Under current 3GPP specifications, the applicable timer value for satellite access is selected according to the satellite NG-RAN radio access technology (RAT) type. However, control paths within the same low Earth orbit (LEO) network can differ in propagation delay and processing load. This article reviews the relevant 3GPP timer semantics and traces the delay components covered by different timers through 5G registration. It organizes candidate refinements along two dimensions: configuration granularity and information scope. A registration case study illustrates the trade-off between avoiding premature timer expiry and recovering promptly from message loss. The article identifies the information and coordination that future specifications would need to address to support interoperable timer configuration.

To support emerging applications such as extended reality, autonomous systems, and large-scale cyber-physical infrastructures, next-generation wireless networks must evolve beyond traditional communication paradigms toward tightly integrated and closed-loop systems. Digital Twin Networks (DTNs) have recently emerged as a key enabler of this transformation by bridging the physical and digital worlds through real-time, bidirectional interactions and adaptive control mechanisms. In this tutorial, we provide a comprehensive overview of DTN design and optimization across multiple system dimensions. Specifically, we first introduce the fundamental design principles of DTN creation, including high-fidelity twin modeling, telemetry integration, and cyber-physical synchronization. We then present resource optimization and semantic-driven DTNs, where application-aware objectives and semantic abstractions guide efficient data processing, communication, and decision-making. Subsequently, we explore physical-layer optimization techniques for DTNs, emphasizing joint sensing, communication, and adaptive wireless resource control. Furthermore, we examine DTN-guided network management and feedback optimization, focusing on closed-loop learning, real-time control, and reinforcement-driven adaptation between digital twins and physical networks. Finally, we review ongoing standardization efforts and enabling tools that support the practical deployment of DTNs in real-world systems. This tutorial aims to provide a unified perspective on DTNs as an emerging paradigm for application-aware, AI-native, and closed-loop wireless systems, and to highlight key challenges and future research directions in this rapidly evolving field.

Real-time synchronized content delivery is critical for applications such as cloud gaming, collaborative mixed reality, and live streaming, where a server must ensure that a group of users perceives the same content simultaneously for each delivery. Due to heterogeneous and unpredictable network delays, ensuring synchronization is highly challenging. We propose a clock-free scheduling framework that introduces latency compensation to control the server's delivery time for each user, aiming to maximize synchronization performance (i.e., minimizing the gap between the maximum and minimum latencies) while maintaining a low overall latency cost. Unlike prior work requiring synchronized clocks or client-side timestamps, our approach uses only acknowledgment feedback, enabling flexible deployment across diverse environments. We formulate the problem as a convex stochastic optimization with a non-smooth objective. To solve the problem, we employ the stochastic proximal point (SPP) algorithm that updates the delay compensation, using only the previous latency samples, rather than the latency distribution. By exploiting the subproblem structure, we apply ADMM to enable efficient updates with per-iteration complexity of at most $\mathcal{O}(N\log N)$, where $N$ is the number of users. We establish a convergence rate of $\mathcal{O}(1/\sqrt{K})$, where $K$ is the number of content deliveries for transmission. The framework is further extended to handle transmission failures, where packet loss results in successful delivery to only a subset of users. Finally, simulations using both synthetic and real-world latency traces demonstrate the consistent superiority of our algorithm against baseline methods.

5G edge network slicing must reconcile two coupled requirements: understanding high-level operator intent and reallocating scarce computing and bandwidth resources under dynamic contention. Centralized optimization can face computational and responsiveness challenges under rapidly changing edge contention, and Multi-Agent System (MAS)-based auctions can support resource arbitration but still depend on operator-defined economic parameters and priorities. Embedding a Large Language Model (LLM) directly in a recurring control loop, by contrast, may introduce non-negligible inference latency and non-deterministic behavior. This paper proposes a two-timescale architecture that addresses this gap: an LLM-based Strategist translates natural-language network intents into economic policy parameters (budget, income, and bidding-aggressiveness) at the macro scale, while a deterministic, Vickrey-inspired second-price mechanism run by SPADE-based slice agents resolves recurring resource contention at the operational scale, with the resulting allocations enforced by patching Kubernetes CPU limits and Calico CNI bandwidth annotations directly on User Plane Function pods. We evaluate the architecture end-to-end on a real Kubernetes/Open5GS edge cluster across two complementary scenarios, each repeated 30 times: a Smart-City scenario, in which natural-language public-safety intents propagate into measurable changes in slice bids and bandwidth allocations within a few auction rounds, and a Hierarchical-slicing scenario, in which the mechanism provides priority-dependent resource and QoE differentiation, adapts to abrupt demand changes, and preserves configured minimum guarantees while maintaining near-complete allocation of the shared bandwidth pool.

The rapid expansion of generative AI is driving computing infrastructures toward interconnected AI data centers. Multi-band optical networks have become the backbone of such connectivity. To accommodate increasingly dynamic traffic demands, these networks require continual resource re-optimization to improve resource utilization and reduce service blocking. However, multi-band transmission amplifies inter-channel stimulated Raman scattering(ISRS), inducing nonlinear coupling among services. This coupling complicates re-optimization, as local adjustments perturb the global power profile, creating interdependent optimization decisions across services. Moreover, the nonlinear interactions vary with the subset of services selected for re-optimization, and inappropriate selection may reduce re-optimization effectiveness or diminish achievable benefits. To address these challenges, we propose a Stackelberg Game with Multi-Agent Transformer (SG-MAT) approach for ISRS-aware resource re-optimization. SG-MAT employs a leader-follower structure, in which the MAT-based follower models nonlinear coupling and generates coordinated channel reallocations, feeding the utilization improvements to the leader. Using this feedback, the leader identifies critical service subsets, approximating a Stackelberg equilibrium to guide global decisions and enabling consistent and efficient re-optimization. Simulation results show that SG-MAT achieves the highest utilization improvement and reduces the blocking ratio by approximately 30% under moderate traffic loads, with computational complexity comparable to benchmarks.

Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting models over extended periods of time in order to assess their adaptability to unexpected phenomena and shifts in underlying patterns. This kind of long-term evaluation requires multi-year data to account for yearly seasonality and behavioral drift. Existing public datasets are too short to support such long-horizon forecasting tasks. We introduce CESNET-TimeSeries26, comprising 995 days of continuous traffic time series from the CESNET3 network, together with a single subnet spanning approximately 4.5 years. The dataset captures 278,466 IP addresses, 285 institutions, and 566 institutional subnets at three aggregation intervals. The dataset provides a definitive longitudinal benchmark for evaluating forecasting models in both operational management and security monitoring. We demonstrate that annual seasonality is the dominant periodic component. Moreover, we show that a forecasting model can exploit the annual component only when its training window covers at least one complete annual cycle, which no sub-year dataset provides.

Energy-aware orchestration across geographically distributed infrastructures is increasingly important as data centers absorb growing Artificial Intelligence (AI) workloads. However, existing approaches often exclude the energy cost of automated decision-making from system-level sustainability assessments. This paper presents an energy-aware agentic orchestration framework that treats Large Language Model (LLM) reasoning as a measurable component of the control loop. We compare single-shot tool calling, ReAct, and plan-and-execute strategies on a multi-site Kubernetes testbed spanning two European data centers and instrumented at layers ranging from power distribution units to per-pod energy attribution. Using ten natural-language orchestration intents with varying decision complexity, we show that reasoning strategy and intent complexity strongly influence graphics processing unit (GPU) energy consumption during orchestration decisions. ReAct and plan-and-execute generated executable tool calls across all evaluated intents but required multiple LLM inferences, whereas the single-shot configuration was less costly but non-actionable. Prefix caching reduced mean ReAct GPU energy consumption by 29.4%. For the evaluated lightweight workload, the infrastructure-energy changes observed within the one-second measurement window were insufficient to offset the measured GPU decision energy. These findings demonstrate the importance of accounting for reasoning overhead when assessing the sustainability of AI-native orchestration and motivate energy-budgeted selection of agent strategies.

Public advertising transparency relies on a minimum accountability condition: an account authorized by a publisher in ads.txt or app-ads.txt should appear in the applicable sellers.json registry. When an available registry omits the declared seller ID, the authorization remains visible and machine-readable, but its public verification path breaks. We call this failure the seller-identity gap. We measure the gap using 233.4 million normalized advertising-transparency records and an absent-only, coverage-aware evidence rule. A declaration enters the numerator only when the applicable registry is available and omits its seller ID; unavailable, malformed, confidential, and other non-evaluable states remain separate. The resulting row-level floor is 24.25% in a high-popularity, low-removal reference population and 30.71% among publishers associated with at least 100 copyright-removal requests, a 6.45 percentage-point increase. The gap then remains near one third at stricter cumulative thresholds. Standardizing the populations to common weights over 1,469 shared ad systems reduces the primary contrast to 4.65 points: about 72% remains within systems, while 28% forms a composition-associated residual. We connect these population estimates to recurring seller-account coordinates, byte-identical declaration configurations, and provenance-bearing case packets. We frame this declaration-centered investigative use of public advertising records as Ad Transparency Intelligence: it reconstructs declared supply-chain relationships, identifies where public verification stops, and prioritizes evidence for corroboration without equating correlation with attribution. Advertising transparency supports accountability and investigation only when visible authorizations remain publicly verifiable.

Many large enterprise and cloud deployments rely on metrics observability solutions (e.g., Prometheus) to monitor key performance indicators of system health (e.g., CPU, memory, network, app-level statistics). Monitoring and analyzing these metrics presents a significant challenge in terms of cost-latency tradeoffs at scale. Ideally, system operators would like immediately deployable and backwards compatible solutions to improve the cost-latency tradeoffs without significant impact on the fidelity of their observability applications. Unfortunately, existing solutions including state-of-art timeseries databases and custom approximate querying solutions fail on one or more aspects. In this paper, we present the design and implementation of TurboProm, a backwards-compatible drop-in accelerator for metrics observability deployments. TurboProm recasts metrics analytics tasks using a semantic-preserving summary view rather than classical query-centric view. Instead of estimating query results on raw timeseries metrics data, TurboProm uses semantic-preserving summaries that enable high-fidelity analytics with significant cost-latency improvements. We present a practical implementation of TurboProm that can be retrofitted to any existing observability deployment. We evaluate TurboProm on diverse real and synthetic workloads against state-of-art solutions and show that it can provide significant reduction in query latency and cost. TurboProm is open-source at https://github.com/ProjectASAP/ASAPQuery.

Large multi-dwelling unit (MDU), student-housing and hospitality networks rarely store resident pre-shared keys (PSKs) on the access point (AP). Instead, the per-resident keys of ePSK, MPSK, DPSK, iPSK or PPSK services are held in an external RADIUS server or cloud database synchronised with the property-management system. Our earlier work, Companion-BSSID-Assisted PSK Discovery (CAPD), brought WPA3-SAE to multi-PSK networks by learning the client's PSK on a WPA2-only companion BSSID and steering the client with IEEE 802.11v to a hidden WPA2/WPA3 primary BSSID of the same SSID. CAPD assumes that the AP can test every PSK itself. This paper removes that assumption. We propose RADIUS-Assisted Companion PSK Discovery (RA-CAPD). The AP forwards the ANonce, the raw EAPOL-Key message 2 (SNonce, MIC, RSNE), the SSID, the client MAC, the BSSID and the AP MAC to the external server in RADIUS vendor-specific attributes (VSAs). The server validates the MIC against all provisioned keys, identifies the matching PSK, and returns it encrypted under the RADIUS shared secret (or a per-AP key for cloud databases), together with the unit, room or user identity and the VLAN. The AP binds the client to this PSK and identity, synchronises the binding across all APs in the domain, and moves the client to the configured profile: 802.11v-capable clients via a BSS Transition Management request, and other clients by disconnection and reconnection. On that profile, SAE-capable clients complete WPA3-SAE and WPA2-only clients complete WPA2-PSK, both with the learned key and no further search, so the companion BSSID serves only for onboarding. We present the design, attribute dictionary, algorithms, complexity and security properties, together with laboratory validation on commercial access points.

The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterogeneous compute, time-varying communication conditions, and, in some settings, privacy constraints, yet synchronization is often still handled with fixed schedules. However, synchronization decisions are often made outside the client, even though the client directly observes local progress, resource state, and communication conditions. We present a client-side reinforcement learning synchronization framework in which each client decides during training whether to upload, download, synchronize bidirectionally, or defer communication. The framework places a lightweight scheduler alongside the local model and formulates synchronization as a resource-aware control problem driven by local learning and system state. We study this framework on two representative edge workloads, spatiotemporal traffic forecasting and image classification. Across both settings, adaptive client-side synchronization improves the accuracy-cost trade-off, reducing system cost in traffic forecasting while maintaining competitive cost and improving final accuracy in image classification. We also consider privacy-constrained settings, where synchronization must account for not only latency and energy but also privacy expenditure. Overall, our results show that synchronization in edge federated learning is better treated as an adaptive client-side control problem than only as a server-side coordination mechanism.

We consider a multi-hop wireless network where energy-harvesting nodes rely solely on ambient radio-frequency interference as their power source. The goal is to deliver a packet from source to destination with minimum latency while guaranteeing a target end-to-end outage probability under Rayleigh fading. This gives rise to a joint routing and power allocation problem in which interference plays a dual role: it provides harvestable energy, but also impairs link reliability. We first transform the intricate, multiplicative end-to-end outage constraint into an additive form. Then, we propose two optimal algorithms. The first, HOPE, minimizes the bottleneck harvesting latency, i.e., the longest energy-accumulation time along the path, by reducing the problem to a least-cost path computation on a graph with interference-aware link weights. The second, THOPE, extends the objective to total end-to-end latency, adding the cumulative transmission delay, and likewise admits a closed-form least-cost path solution. Both algorithms are solved in polynomial time via Dijkstra's algorithm and yield explicit expressions for the optimal transmit powers. Extensive simulations reveal that HOPE and THOPE reduce the average total latency by up to an order of magnitude compared with shortest-path and minimum-energy benchmarks, while exhibiting sharply concentrated delay distributions that are largely insensitive to interferer density.

Safety-critical Cellular Internet of Things (CIoT) systems increasingly rely on microservice architectures orchestrated across heterogeneous edge and cloud platforms. Although this paradigm improves modularity and scalability, the fault-tolerance mechanisms of contemporary orchestration platforms remain predominantly reactive, i.e., failed components are restarted or rescheduled only after an outage has already occurred. Such recovery can produce multi-second End to End Service Outage Time (E2E SOT), which can grow further under container restart backoff. This paper proposes an Availability Management Framework (AMF), a generic monitor-predict-recover loop that complements reactive recovery with machine learning-based anomaly detection and make-before-break switchover to a standby replica. Candidate ML models are evaluated offline under three protocols, including a change of operating regime, in order to select a model for live deployment. AMF is instantiated and evaluated on a three-tier tele-operated driving (ToD) testbed under a staged resource-exhaustion scenario. Relative to Kubernetes-only recovery, AMF completes switchover during the anomaly window, reduces E2E SOT, and avoids the tens-of-seconds outages associated with repeated in-place restarts under backoff. On this non-real-time testbed, residual E2E SOT lies inside healthy-path jitter and is therefore limited by path timing rather than by lateness of the recovery decision. The paper whows that coupling a validated ML based anomaly detector with proactive switchover can remove restart- and backoff-dominated outage from a critical path.

Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and reduce congestion. Although AI-driven traffic management has shown strong promise, its success relies on diverse, high-quality data spanning critical scenarios such as collisions, near-collisions, and traffic rule violations. Digital twins offer a powerful solution by enabling synchronized virtual replicas of physical traffic environments for real-time monitoring, collision prediction, traffic-flow optimization, and diverse realistic scenario generation. However, continuously synchronizing a high-fidelity urban traffic digital twin through edge surveillance cameras requires substantial edge-cloud communication, and transmitting raw video streams leads to severe bandwidth and latency constraints. To overcome this challenge, this paper proposes a semantic communication-based edge-cloud framework for real-time urban traffic digital twin applications. By leveraging edge intelligence to extract and transmit only task-relevant semantic traffic information instead of full video feeds, the proposed framework significantly reduces communication overhead while preserving the essential dynamics needed for accurate real-time digital twin reconstruction. Experimental results demonstrate more than 99% transmission savings and an average trajectory reconstruction error of only 0.12 m, confirming the proposed framework as an effective, scalable, and low-latency solution for AI-enabled urban traffic digital twin applications.

Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundle Protocol enables store-carry-forward communication, requiring relay nodes to manage limited storage and scarce contact opportunities. Relay-side scheduling and dropping decisions are inherently goal-oriented: transmission resources should be allocated to bundles most valuable to downstream mission objectives. Conventional schedulers, such as FIFO, do not explicitly optimize task utility and may provide unfair service across heterogeneous planetary sources. This paper presents a closed-loop modeling and learning framework for relay-side bundle and flow management in a contact-driven deep-space network, where lunar and Martian updates are forwarded through a shared-buffer relay to Earth. We quantify destination-side information freshness using excess age $\Delta_i^e(t)=[\Delta_i(t)-d_i(t)]^+$, where $\Delta_i(t)$ denotes the Age of Information of source $i$ and $d_i(t)$ is a source-dependent delay baseline. Based on this metric, we define a goal-oriented loss that supports source-dependent nonlinear penalties, allowing prioritization according to mission relevance. We formulate relay control as a constrained semi-Markov decision process (CSMDP) and solve it using masked Lagrangian Proximal Policy Optimization (PPO). Feasibility-aware action masking restricts transmissions to bundles that can complete within the remaining contact window. DTNSim simulations indicate improved age-based utility and inter-flow fairness compared with baseline schedulers. The proposed framework jointly optimizes relay-side bundle scheduling, dropping, and flow control under contact-driven dynamics with explicit fairness considerations across heterogeneous sources.

Achieving low delay and high data rate communication in 5G requires preventing persistent queue backlogs at path bottlenecks. A key indicator of queue buildup is an increase in one-way delay (OWD) of packets. However, accurately measuring OWD is challenging due to synchronization issues between the sender and receiver. Moreover, using OWD to infer the onset of queueing can be inefficient. For instance, congestion control mechanisms that rely on latency as a congestion signal may result in cellular network underutilization, see, e.g., [1]. Thus, to develop more efficient rate control approaches, it is crucial not only to estimate OWD accurately but also to perform a more fine-grained analysis that provides detailed insights into the queue status. This paper presents a framework for modeling OWD in 5G edge networks, where the wireless link is typically the path bottleneck. %, by modeling it through measurable delay components. We demonstrate that OWD is dominated by queueing delay (QD), particularly at high data rates. We analyze QD by decomposing it into two main components: aggregation delay and channel access time. Aggregation delay can be accurately measured at the user equipment (UE), and channel access time can be estimated based on network conditions. Our key contribution shows that QD, while challenging to measure directly at the base station (BS), can be closely approximated by combining these components. We validate this model through NS3 simulations under varying network loads and single- and multi-UE scenarios. This framework provides a foundation for developing advanced mechanisms for congestion control and bottleneck detection in 5G networks.

Many 6G use cases such as tactile Internet and eXtended reality applications would simultaneously require a high data rate and low latency wireless connectivity.It is increasingly being realized that the current transport protocols relying on packet loss and delay are insufficient to satisfy the data rate and latency requirements of such applications. To address this problem, we propose RAPID, a transport layer solution designed for private 5G edge networks, where the bottleneck is located at the base station. We show that by regulating the send rate to maintain a target physical resource block utilization, we can achieve high data rate, low latency communication. We design a simple yet effective policy that adapts to the dynamic nature of wireless channel conditions. In a multi-UE scenario with two RAPID flows and one TCP Cubic flow, our testbed results show that RAPID flows achieve significant improvements in one-way delay, i.e., 72x and 55x lower compared to TCP Cubic, with negligible packet loss at the cost of modest goodput reductions of 16% and 27%, respectively.

Low Earth Orbit (LEO) satellite constellations are expected to play a key role in future 6G Non-Terrestrial Networks (NTNs), where dense deployments enable cooperative multi-satellite processing for communication and positioning. While existing works have addressed communication and localization objectives separately, the correlation and alignment between these objectives remain largely unexplored. In this paper, we investigate the correlation between Spectral Efficiency (SE) and positioning accuracy in cooperative multi-LEO satellite clusters. We consider an uplink direct-to-device scenario in which multiple satellites jointly process user signals and evaluate cluster performance using SE and Position Error Bound (PEB) metrics. We derive Time-of-Arrival (TOA)-based and Doppler-based PEBs, while communication performance is quantified through the achievable SE under joint signal processing. Using realistic LEO constellation simulations, we demonstrate that for clusters of seven satellites, SE exhibits a strong negative correlation with Doppler-based PEB (correlation coefficient of $-0.72$). In contrast, its correlation with TOA-based PEB is notably weaker, ranging between $-0.55$ and $-0.37$. These findings suggest that communication-oriented LEO clusters are largely compatible with Doppler-based positioning, but not necessarily with TOA-based positioning.

Traffic efficiency and air pollutants are major issues in urban transport systems, as they are critical points for traffic flows and people living in urban areas, respectively. In response to this challenge, connected and autonomous vehicles (CAVs) have garnered increasing attention, with the potential to address the above challenges through cooperative maneuvering and real-time communication. In this paper, we consider an intersection served by a multi-access edge computing (MEC) where CAVs communicate with a controller to pass the intersection without the need of signals and without stopping unless in presence of heavy congestion. For this purpose, we propose a first-in-first-scheduled (FIFS) algorithm, and we specify the communication protocol for its practical implementation. The solution is validated through simulations with varying traffic densities, showing that it provides significant gains in travel time and emissions compared to both uncontrolled intersections, with or without traffic lights, and a benchmark solution derived from the literature.

Executing large network configuration changes, called migrations, is slow and error-prone today; operators must manually devise a safe order for updating devices that does not disrupt traffic and meets operational intents (e.g., low total time). We develop Smoothie, a versatile migration planning framework that, unlike existing tools, can support diverse operational intents. Inspired by automated planning, it achieves versatility by decoupling the encoding of network safety and intents from the search for a viable plan. Smoothie uses symbolic simulation for general and scalable safety encoding, after which it can use a range of planners, including existing off-the-shelf planners and a new one we developed for optimized parallel plans. We demonstrate Smoothie's versatility and benchmark its performance using diverse migration scenarios on many topologies. It finds both sequential plans and parallel plans, and the parallel ones compress up to thousands of configuration updates into no more than 3 steps. Its versatility does not come at the expense of performance: finishing most planning tasks in less than 1 hour.