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Computer Science Networking

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Saturday 5 September

Networking

Semantic Communication for Distributed Spectrum Monitoring over Unreliable Links

Samer Lahoud and Kinda Khawam

Distributed spectrum monitoring relies on spatially separated receivers to infer the state of the radio environment. A central task is to determine how many emitters are active and where they are located. Sensors typically report over shared unreliable links, where contention, fading, or energy limits erase whole reports, so forwarding raw I/Q samples from all receivers to a fusion node becomes impractical. This paper formulates distributed emitter counting and localization as a semantic communication problem over unreliable reporting links. Each receiver maps its local time-frequency observation to a compact latent representation and transmits it. The fusion node pairs each surviving latent with the corresponding receiver position and operates on the resulting unordered set, which keeps the fusion rule compatible with variable membership and receiver-count changes. The encoder and decoder are trained end-to-end with report erasure, and optionally finite-rate quantization, inside the task objective. Experiments on synthetic multi-emitter scenes show that channel-aware training improves robustness under erasure, that set fusion is more robust than fixed-order concatenation, and that one trained model operates across receiver counts without retraining. The rate study shows that a few bits per latent component suffice in the tested setting, and that the rate knee remains roughly stable across erasure levels. These results support the feasibility of low-rate semantic reporting for distributed spectrum monitoring over unreliable links.
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Friday 4 September

Networking · Cross-listed from cs.GT

Strategic Information Transmission over Gossip Networks

Emirhan Tekez, Melih Bastopcu and Sinan Gezici

We consider a fully connected gossip network of $n$ nodes that track a binary continuous-time Markov source through a strategic sender transmitting updates under a communication budget at a rate that depends on the source state. The receivers exchange packets through gossip and decide whether to follow the sender. We model this interaction as a Stackelberg game and analyze it through a stochastic hybrid systems (SHS) framework. We prove that the sender's budget constraint binds at every interior optimum, reducing its problem to a one-dimensional search on the budget line. When the sender pushes its preferred state at the higher rate, the receivers gossip at the highest available rate. Gossip has no direction of its own and works against the asymmetry in the sender's policy rather than reinforcing it. We prove that an optimistic Stackelberg equilibrium exists, and that it is unique and explicitly characterized whenever a policy on the strategic half of that line is feasible at the gossip cap. Monte Carlo simulations agree with the analytical recursion.
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Networking

SDN-Orchestrated Dual-Path 5G/SATCOM Maritime Communications for Carrier Strike Groups

Avinash Srinivasan, Dalibor Španjević, Kevin H. Nguyen et al. (+2 more)

Carrier Strike Group (CSG) communications must sustain mission traffic across heterogeneous links whose quality varies with distance, weather, fading, and intermittent outages. We present an integrated Software-Defined Networking (SDN)/Software-Defined Radio (SDR) framework that unifies 5G and satellite communications (SATCOM) under a single control plane for dual-path failover in CSG communications. The framework is evaluated via stochastic simulation across four operational regions: Norfolk, the Norwegian Sea, the Philippine Sea, and the North Pacific storm track. The framework couples a centralized SDN controller with a Rician-faded 5G channel under Adaptive Modulation and Coding (AMC), a Ku-band SATCOM channel with ITU-R P.618/P.838 rain attenuation, and Gilbert-Elliott burst-availability processes on both links. The controller routes each packet by traffic class and current link state. Experiments use 60 independent trials of 50,000 packets per parameter setting with common random numbers, paired confidence intervals, and convergence diagnostics across fault, fading, and region sweeps. For the Norfolk weather baseline, SDN routing reaches 93.09% reliability versus 36.22% (5G-only) and 89.87% (SATCOM-only), with paired gains of +56.87 and +3.22 percentage points. The advantage grows under joint low-Rician-K/high-burst-fault stress, and AMC improves 5G robustness as Signal-to-Interference-plus-Noise Ratio degrades. SDN remains the highest-reliability mode across all four regions, indicating that cross-layer SDN-controlled failover improves maritime resilience without relying on deterministic link abstractions.
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All authors Avinash Srinivasan, Dalibor Španjević, Kevin H. Nguyen, Bannon Ireton, Christopher B. Landis

Networking

WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO

Marcin Hoffmann and Paweł Kryszkiewicz

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.
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Networking · Cross-listed from cs.CR

XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks

Adeel Ahmad, Ali Akarma, Ahmad Ali et al. (+2 more)

One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller can make an entire network unusable. To address these challenges, we suggest an entropy-guided machine learning framework, called XAI-SDN, for real-time DDoS detection in SDN environments which is lightweight and explainable. The framework extends the flow features extracted by CICFlowMeter with eight Shannon entropy metrics obtained by an $\mathcal{O}(1)$ rolling algorithm and uses a Random Forest classifier with SHAP TreeExplainer for providing transparency at the prediction level. On a fixed temporal split, XAI-SDN achieves an accuracy of 99.9987%, a macro F1-score of 99.9621%, and an AUC-ROC of 1.0000 on the full 3.59 million flows of the CIC-DDoS2019 SYN benchmark. The pipeline sustains 0.0165~ms per flow (60{,}606 flows/s) without the use of SHAP and 0.5122~ms per flow (1{,}953 flows/s) with full support of SHAP under the 99.14% prevalence of DDoS traffic, which is a step towards achieving a balance between the detection performance and operational transparency in next-generation SDN security.
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All authors Adeel Ahmad, Ali Akarma, Ahmad Ali, Hammad Muneer, Toqeer Ali Syed

Networking

Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks

Sarath Babu, Victor Baños-Gonzalez, Mario Cordina et al. (+20 more)

This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satellite systems to cooperate through agreements, potentially unified satellite terminals, and common resource management. We show that system federation significantly enhances both latency performance and connectivity robustness compared with non-federated LEO architectures. This paper also investigates the challenges and possible solutions for adopting the Open-RAN architecture for T-NTN, including routing options for mega-LEO systems, edge intelligence, and energy efficiency as critical elements for sustainability. Finally, we address the security, privacy, and resilience aspects of federated T-NTN architectures with emphasis on zero-trust, secure routing, trustworthy edge intelligence, Post-Quantum Cryptography (PQC), and Quantum Key Distribution (QKD).
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All authors Sarath Babu, Victor Baños-Gonzalez, Mario Cordina, Debabrata Dalai, Tomaso de Cola, Franco Davoli, Etienne Victor Depasquale, Ashutosh Dutta, Hesham ElBakoury, Michael A. Enright, Giovanni Giambene, Sumit Goswami, Ramesh Gupta, Wael Jaafar, Eman Hammad, B. S. Manoj, Tony Li, Manuel M. H. Roth, Paresh Saxena, Pat Scanlan, Zhili Sun, Daniele Tarchi, Saviour Zammit

Networking · Cross-listed from cs.LG

Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

Abdessamed Qchohi, Jessica Moysen Cortes and Matteo Zecchin

Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that would have been observed under alternative control actions. A key challenge is that logged telemetry may omit variables used by the controller, resulting in hidden confounding and invalidating the statistical guarantees of counterfactual analysis. In principle, this issue can be addressed using randomized telemetry, collected by assigning control actions independently of the network state. However, because such randomization may disrupt normal operation, randomized telemetry is typically scarce, causing counterfactual analysis based solely on it to produce uninformative prediction sets. To address these challenges, we propose Confounding-Valid Counterfactual Conformal Inference (CV-CCI), which combines abundant, potentially confounded observational telemetry with limited randomized data through the General Synthetic-Powered Inference (GESPI) principle. CV-CCI leverages observational data to improve efficiency while using randomized data to retain finite-sample coverage guarantees under arbitrary hidden confounding. Experiments on two representative radio access network (RAN) control tasks show that CV-CCI remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art confounding-valid baselines.
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Networking

On the Delay-Constrained Maximum Concurrent Flow Problem

Walid Ben-Ameur, Guillaume Beraud-Sudreau, Hervé Kerivin et al. (+1 more)

Real-time services, such as VoIP and large-scale neural network training, require strict transmission delay guarantees. While routing under hop constraints is tractable, real-world delays increase sharply with equipment load, typically modeled using the M/M/1 queuing function where delay is inversely proportional to available bandwidth. We investigate the resulting Delay-Constrained Maximum Concurrent Flow (DCMCF) problem, which seeks to maximize the minimum throughput across all commodities. The problem's complexity stems from the conditional and non-linear nature of the delay constraints, which are active only along the specific paths used by the flow. We prove that DCMCF is strongly NP-hard, even for single-source/single-destination instances. To address the inherent non-convexity of the problem, we introduce a new convex relaxation expressed through second-order cone constraints, obtained from the convex envelope of a function representing the conditional delay associated with a single arc of a given path. The relaxation is shown to outperform existing formulations based on disjunctive programming. Leveraging this result, we develop a polynomial-time approximation algorithm with a provable performance guarantee and present numerical experiments demonstrating the effectiveness of the proposed approach.
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All authors Walid Ben-Ameur, Guillaume Beraud-Sudreau, Hervé Kerivin, Sebastien Martin

Networking

Performance Evaluation of HAPS-enabled Coverage Enhancement in Hard-to-Reach Areas

Hao Lin, Mustafa A. Kishk and Mohamed-Slim Alouini

High altitude platform stations (HAPSs) are becoming a key component of future non-terrestrial networks (NTNs). HAPSs can serve a larger area than uncrewed aerial vehicles (UAVs) and offer lower propagation latency, maintenance expense, and energy costs than satellites. A major application of HAPSs is to serve the areas where terrestrial network (TN) deployment is infeasible, especially in hard-to-reach areas and post-disaster areas. For instance, in the Amazon rainforest, the Mediterranean region, and deserts, TN deployment is severely constrained by geographical and environmental conditions. Only areas close to transportation networks or coastlines can be covered, while large areas remain uncovered. Such coverage holes in hard-to-reach areas are typically overlooked in existing literature. Motivated by these realistic cases, in this paper, we use tools from stochastic geometry to mathematically model hard-to-reach areas where cellular terrestrial infrastructure only exists at their perimeter. We propose to deploy a HAPS constellation over this hard-to-reach area to enhance connectivity. For that setup, we derive the downlink (DL) and uplink (UL) coverage performance of the considered user equipment (UE) as a function of the location of the UE inside the coverage hole. Our results show how the number of HAPSs, beamwidth, and HAPS altitude affect the DL and UL coverage probabilities. Finally, we provide multiple useful guidelines for future HAPS deployment.
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Networking

QUASAR: Quantum Satellite Architecture and Routing Simulator

Yaliang Shi, Zi Wang, Bangguo Yuan et al. (+2 more)

The deployment of Low Earth Orbit (LEO) satellite constellations is an important step toward global-scale quantum networking. However, evaluating satellite quantum network protocols under spatiotemporal orbital dynamics and quantum physical constraints remains computationally expensive and challenging. In this paper, we propose QUASAR, a lightweight simulator for evaluating entanglement distribution in satellite-based quantum networks. QUASAR provides a decoupled architecture that integrates dynamic orbital topologies, time-varying optical transmittance, and quantum memory decoherence into network- layer attributes. To demonstrate its capabilities, we abstract and implement two representative hardware architectures: Simultaneous Downlink and On-Orbit Stitching. We further introduce an Entanglement Distribution Rate (EDR)-Aware Spatiotemporal Routing (EASR) heuristic as a reference workload. Our case study examines how QUASAR supports different satellite architectures, routing workloads, realistic orbital traces, concurrent requests, and scalable event-driven execution. With over 85% lower network-layer update latency than continuous polling, QUASAR provides a practical and extensible framework for future satellite quantum network protocol evaluation.
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All authors Yaliang Shi, Zi Wang, Bangguo Yuan, Gaojie Wu, Zhiwei Zhao

Networking

A Wavelength Borrowing Architecture for Optical Data Center Networks - Extended Version

Andrea Detti, Chiara Lodovisi and Silvello Betti

The growth of east-west traffic, along with the cost and power consumption of electronic switching, is motivating the integration of a low-power, high-rate, all-optical layer within the data center network. This paper presents a spine-leaf all-optical architecture in which the default wavelength configuration, one wavelength per source–destination leaf pair, can be reconfigured to accommodate unbalanced traffic demand: wavelengths that are unused or lightly loaded at one leaf are borrowed by another leaf with higher demand. This topology-engineering capability is combined with a traffic-engineering scheme, based on two-hop detouring, enabling the control of wavelength load while limiting the amount of detoured traffic. A key feature of the architecture is that its degree of wavelength reconfigurability is set by a single tunable parameter, the borrowing degree $B$, ranging from none to full; performance evaluation shows that near-optimal performance is achieved well below the maximum $B$, saving the complexity and cost of fully reconfigurable solutions. Furthermore, the optical fabric relies on mature, data-center-grade components, namely AWGs, AWGRs, colorless OXCs, and combiners, whose reconfiguration speed makes the architecture deployable at network tiers where traffic demand persists over seconds or longer, e.g., among groups of racks (pods). The architecture is also TDMA-transparent, a property that future work could exploit to refine the borrowing unit below a whole wavelength without changing the optical fabric.
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Thursday 3 September

Networking · Cross-listed from cs.AI

ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality

Yoga Suhas Kuruba Manjunath, Jie Gao and Lian Zhao

We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) risk. ResLearn-XR adopts a two-stage temporal learning structure comprising a base sequence prediction model augmented with task-specific residual learning components to improve adaptability to bursty, non-stationary XR traffic dynamics. The residual learning stages operate in the value space for continuous XR traffic forecasting and in the logit space for probabilistic QoE risk estimation. \rev{For the QoE-risk branch, we introduce a Data Descriptor Algorithm (DDA), a causal feature-construction module that converts packet-level application-layer observables into frame-timing-aware descriptors suitable for encrypted traffic analysis. We also construct an XR Traffic-QoE dataset that pairs continuous XR traffic traces with session-level user-reported QoE labels. ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction, while reducing QoE-risk estimation SMAPE by up to 87.8% over single-stage baselines.
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Networking

Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity Localization

Ala' A. Alsamarneh and Omar Alhussein

Intent-based networking realization starts by translating high-level intents into low-level network configurations. Recent approaches have shifted toward LLM-based translation. Despite promising results, most studies focus on translation accuracy and overlook risks associated with deploying the resulting configurations. In this work, we investigate the pre-deployment translation risk of LLM-generated configurations by analyzing the model's uncertainty. We propose to use two uncertainty signals, namely sampling-based predictive uncertainty for translation-risk ranking and token-level entropy for ambiguity-source localization. We evaluate these signals on an ambiguity-controlled test set across different context types and sampling budgets, using a Llama-3.1-8B-Instruct model fine-tuned for intent translation on a vendor-specific switch platform (Juniper EX3300). The results demonstrate that predictive uncertainty provides a useful signal for ranking translations by risk across context types and sampling budgets, albeit with substantial miscalibration under less informative contexts. Moreover, we show that parameter-token entropy correlates with parameter-sourced ambiguity and keyword-token entropy correlates with description-sourced ambiguity. These results indicate the potential of using uncertainty signals in an LLM-generated configuration deployment pipeline, where predictive uncertainty can support selective deployment, while token-level entropy can identify sources of ambiguity.
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Networking

Waves on the Walls: Empirical Characterization of mmWave Lateral Waves for Enhanced Indoor Coverage

Apala Pramanik, Avhishek Biswas, Sasitharan Balasubramaniam et al. (+2 more)

High-frequency millimeter-wave (mmWave) communication systems are constrained by the surrounding environment, where walls are traditionally treated as obstacles that block or reflect signals indoors. Consequently, current beamforming strategies are tailored to circumvent these obstructions. In this paper, a paradigm shift is introduced that leverages lateral wave propagation along building interfaces to extend mmWave coverage. Unlike traditional reflections, lateral waves travel along the boundary between two media of different refractive indices and decay algebraically with distance, offering a potential alternative path for mmWave connectivity. While well-established at low frequencies in natural media, the existence of lateral waves at mmWave frequencies along engineered building materials has not been demonstrated before. To this end, the first experimental characterization of mmWave lateral waves along a wall is reported. Extensive controlled measurements are employed to characterize the signal-grazing geometry and to establish a frequency and distance-dependent path-loss model for this phenomenon. The results provide the first empirical foundation for a new class of interface-guided mmWave links.
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All authors Apala Pramanik, Avhishek Biswas, Sasitharan Balasubramaniam, Christos Argyropoulos, Mehmet C. Vuran

Networking

Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters

Eashan Gupta, Yongzhou Chen, Apoorve Mohan et al. (+3 more)

Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communication round to slowdown. Current approaches for evading congestion in AI clusters assume global control over the entire workload (e.g. coordinating the schedule of all jobs) or assume infrastructural support (e.g. adaptive routing in switches). They are thus ill-suited in a shared cloud setting where AI jobs belonging to one user can face external congestion from other users' jobs or background traffic beyond its own control. In this paper, we build a system, REACT, that tunes the recurring pattern of data exchange between GPU nodes (known as communication collectives) in response to congestion. REACT works at the application (communication library) layer, where it detects congestion at runtime using readily available flow stats, and tunes the collective pattern to alleviate congestion - changing the set of incident flows while retaining the semantics of information exchange (e.g. selecting which node aggregates data in an AllReduce tree). REACT requires no explicit support from the underlying network infrastructure and can be unilaterally deployed by individual users in a shared cloud setting. We prototype REACT as a shim layer over NCCL, and evaluate it on a shared academic GPU cluster - enabling REACT improves communication performance (algorithm bandwidth) by 13%-38% under network congestion. Our simulations across a range of congestion scenarios further reveal up to 75% performance improvement, highlighting the effectiveness of our approach.
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All authors Eashan Gupta, Yongzhou Chen, Apoorve Mohan, Pavlos Maniotis, Abdullah Kayi, Radhika Mittal

Networking

Network Availability Enhancement in Low-Altitude HetNets: A Cross-Layer Design Perspective

Teng Wu, Jiandong Li, Junyu Liu et al. (+4 more)

This paper proposes a computing-communication resource interchange method to enhance network availability (NA) in low-altitude heterogeneous networks (LA-HetNets). In these networks, communication resource conflicts and imbalances, caused by extreme heterogeneity (diverse mobility, mixed delays, and hybrid transmission), and cross-regional traffic, reduce reliability and lead to unavailability. Restoring NA requires additional communication resources, yet dynamic cross-regional scheduling is limited, making locally redundant computing resources an alternative to reduce communication resource overhead. While computing resources address medium access control (MAC)-layer unreliability, physical (PHY)-layer functionalities still rely on communication resources. Thus, it remains unclear whether increasing computing resources alone can achieve target NA, especially under greater heterogeneity. We elaborate on the impact of heterogeneity on NA and show that expanding computing resources alone cannot meet target NA under high heterogeneity, as NA degrades sharply due to increased communication capability demands. To overcome this, we propose a cross-layer optimization method enabling computing-communication resource interchange to address both MAC- and PHY-layer unreliability. By reducing processing delays with computing resources while ensuring MAC-layer reliability, our method extends PHY-layer transmission delay and expands communication resources. Simulations demonstrate our approach's superiority in achieving target NA under greater heterogeneity, revealing that computing-communication resource interchange fulfills expanding communication capability demands more effectively than conventional resource overhead reduction.
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All authors Teng Wu, Jiandong Li, Junyu Liu, Min Sheng, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

Networking

Is Collision-Free Backoff Worth It in Wi-Fi?

Mohammad Yousefi, Francesc Wilhelmi and Boris Bellalta

The Distributed Coordination Function (DCF)—the underlying channel access protocol in Wi-Fi, based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and Binary Exponential Backoff (BEB)—is simple and effective, but its performance can degrade with increasing contention due to collisions. Collision-free backoff algorithms replace randomization with deterministic channel access, potentially improving efficiency and predictability. But are collision-free methods, with all their implications in terms of protocol design, sufficient to improve Wi-Fi performance under realistic non-full-buffer traffic with both uplink and downlink transmissions? We investigate this question by comparing three collision-free algorithms, CSMA/ECA (ECA), CSMA/E2CA (E2CA), and deterministic backoff (DetBO), with standard and single-stage BEB. Using system-level simulations, we evaluate full-buffer traffic, non-full-buffer ON/OFF traffic with both uplink and downlink transmissions, and coexistence with legacy BEB stations. Under full-buffer traffic, collision-free operation provides only modest throughput gains, up to 6.3%. With non-full-buffer traffic, delay is governed primarily by the contention window (CW) rather than by collision avoidance, and collision-free access can even degrade performance under downlink-heavy traffic. Stations fall back to a random backoff at least 93% of the time, so a collision-free schedule rarely forms outside the AP.
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Networking

Employing the Structural Power to Achieve Supply-Demand Balanced Payment Channel Networks

Shuyao Xiao, Shengling Wang, Hongwei Shi et al. (+2 more)

Blockchain technology faces scalability challenges because transactions must be validated and recorded across the network. Payment channel networks (PCNs) improve efficiency by moving transactions off-chain and recording only critical interactions on the mainnet. However, PCNs require pre-deposited channel balances (supply) to match transaction demands (demand), and insufficient balances cause supply shortages. Existing approaches, including transaction path optimization and channel balance allocation, address this problem but incur high costs due to dynamic adaptation to fluctuating demands. We reveal a correlation between balance deficits in PCNs and network topology by uncovering the structural organization of liquidity allocation. This enables supply-demand conflicts to be mitigated from a static topological perspective through channel reconfiguration, without additional balance replenishment. We further introduce payment topological entropy (PTE), an information-theoretic metric that quantifies each node's deviation from the global average connection pattern and captures structural properties of balance supply. Based on PTE, we design MaxPTE, a topology optimization algorithm that reorganizes balance allocation across channels through structural reconfiguration, aligning static balance distribution with dynamic transaction demand. Extensive experiments show that MaxPTE reduces balance deficits by 27.25%, increases average maximum flow by 12.23%, and decreases transaction failure probability by 25.96%, outperforming existing benchmarks. The method also remains robust across diverse balance-demand distributions, improving supply-demand balance without prior demand prediction.
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All authors Shuyao Xiao, Shengling Wang, Hongwei Shi, Weicheng Wang, Anlin Chen

Networking

From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control

Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino et al. (+2 more)

Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is essential for NextG interactive applications, yet providing strict End-to-End (E2E) peak latency guarantees remains an open challenge. Two obstacles limit the adoption of learning-based network control in this setting: traditional volume-based routing metrics, while highly effective for general traffic management, are not designed to capture traffic urgency; and Deep Reinforcement Learning (DRL) controllers trained from scratch suffer from sample inefficiency, long training times, and early-stage exploration volatility. This paper introduces a deployment-focused network control framework that addresses both obstacles. First, we present Effective Congestion (EC), a deadline-aware metric family that quantifies interface congestion by packet urgency and proactively filters non-viable traffic, coupled with a Uniform Path Grouping (UPG) distribution heuristic promoting robust load-balancing; the resulting policies are embedded into Multi-Agent Deep Reinforcement Learning Effective Congestion ($p^*$) (MADRL EC ($p^*$)), a hybrid architecture combining a distributed scheduler with a centralized RL-based router. Second, we introduce a unified training objective that generalizes existing policy-learning paradigms—behavioral cloning, offline Reinforcement Learning (RL), online RL, and offline-to-online schemes—as special cases, combining a live-reward term, a pre-collected-reward term, and a policy-imitation term. From this objective, we derive the Model-Guided Annealed Reinforcement Learning (MGA-RL) protocol, instantiated on a Deep Deterministic Policy Gradient (DDPG) backbone: a deployment-oriented, demonstration-driven training approach that generalizes conventional Offline-to-Online (O2O) schemes, in which trajectories from a lightweight [...]
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All authors Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca

Networking

A Semantic-Aware Multiple Access Scheme Leveraging Spatial Redundancy for Uplink-Dominant Network Services

Hamidreza Mazandarani, Masoud Shokrnezhad and Tarik Taleb

The transition toward semantic-aware communication offers a paradigm shift for next-generation mobile networks, promising to decouple information significance from raw data transmission. Despite advances in semantic extraction, the integration of semantic intelligence into the Medium Access Control (MAC) layer remains underexplored, particularly in exploiting spatial correlations among users. To address this, we introduce a novel multiple access scheme designed for uplink-dominant network services. This framework optimizes the trade-off between spectrum utilization and sustainability by formulating variable-packet-length access as distinct $\alpha$-fairness and energy efficiency problems. A key innovation of our approach is the quantification of spatial redundancies through novel metrics of self-throughput and assisted-throughput, which account for the semantic correlation of data across user equipment. We analyze these formulations to identify optimal bounds before proposing PRISM (Protocol for Redundancy Identification in Semantic Multiple-access). Grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), PRISM enables devices to autonomously govern spectrum access using only local observations. Extensive evaluations demonstrate that PRISM successfully leverages redundancies to outperform semantic-oblivious schemes, achieving up to \({90\%}\) of the centralized optimal benchmark and improving both objectives by up to \({2\times}\) across diverse user-semantic association matrices. These results validate PRISM as a viable candidate for future distributed mobile network applications, complemented by orthogonal Multiple Access Schemes where signals are multiplexed in the semantic domain.
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Networking · Cross-listed from cs.LG

An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data

Mahshid Rezakhani, Tolunay Seyfi and Fatemeh Afghah

Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain variability across environments. In this paper, we propose a novel framework for multivariate time-series anomaly detection that leverages adversarial learning and contrastive loss within a sequence-based Variational Autoencoder (VAE) architecture. Our method enables zero-shot domain adaptation by jointly optimizing domain-invariant latent representations and semantically structured embedding spaces, without requiring labeled data or raw feature transfer. To address the heterogeneity of IoT deployments, we introduce encoder and decoder adaptor layers that align feature distributions across domains while preserving contextual semantics. Additionally, we propose a destination-based segmentation strategy to better model real-world communication structures in IoT traffic. Our framework is comprehensively evaluated on six distinct datasets spanning industrial, enterprise, general-purpose, smart home, and military automation domains across 44 transfer scenarios. Experimental results demonstrate strong zero-shot generalization in several cross-domain settings and competitive performance against a contrastive domain-adaptation baseline under realistic, heterogeneous, and privacy-constrained IoT conditions.
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Networking

Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR

Leonardo Spampinato, Mahamadou Togola, Matteo Bernabè et al. (+3 more)

Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and complexity of beam-based 5G new radio (NR) networks make accurate performance forecasting highly challenging. This paper proposes a data-driven approach to predict the average downlink signal-to-interference-plus-noise ratio (SINR) relying exclusively on standardized reference-signal measurements, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS) reference signal received power (RSRP). We formulate this prediction as a supervised learning problem and evaluate various input feature representations using a third generation partnership project (3GPP)-compliant synthetic dataset. Our analysis reveals that filtering measurements based on active CSI-RS beams significantly enhances prediction accuracy while reducing input dimensionality. This activity-aware strategy demonstrates the strong viability of machine learning models for proactive network optimization.
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All authors Leonardo Spampinato, Mahamadou Togola, Matteo Bernabè, Azim Akhtarshenas, Lorenzo Mario Amorosa, David López-Pérez

Wednesday 2 September

Networking

Bringing dApps to OCUDU: An E3 Controller for Real-Time Open RAN Intelligence

Angelo Feraudo, Ravis Shirkhani, Stefano Maxenti et al. (+3 more)

Real-time control loops in Open RAN are increasingly colocated with the gNB, where dApps, i.e., programmable applications with sub-millisecond access to PHY- and MAC-layer signals, enable latency-critical use cases such as spectrum sharing, channel-aware scheduling, and integrated sensing. What turns dApps from a per-vendor mechanism into a portable component of the emerging AI-RAN ecosystem is the E3 interface: this interface defines how a dApp subscribes to RAN telemetry, receives indications, and issues control actions back to the RAN. To date, E3 has been implemented on OpenAirInterface and on NVIDIA Aerial; OCUDU, i.e., the Linux Foundation's open-source CU/DU project, has lacked a comparable E3 path. We close that gap with an open-source E3Controller for OCUDU, designed as a sidecar daemon so the E3 protocol stack, service-model logic, and dApp interactions live entirely outside RAN core processing, which is touched only through well-defined instrumentation hooks. We validate the controller with two reference service models, Spectrum and L1, that deliver O-RAN fronthaul I/Q samples to a spectrum-sensing dApp over a Foxconn RPQN 4800 radio unit. Our evaluation shows that the E3 path imposes no measurable throughput penalty on the OCUDU RAN, demonstrating that a real-time dApp/RAN interface is feasible on production-grade open RAN software.
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All authors Angelo Feraudo, Ravis Shirkhani, Stefano Maxenti, Andrea Lacava, Michele Polese, Tommaso Melodia

Networking

Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

Mehdi Zafari, Iman Mohammadi and A. Lee Swindlehurst

Learning-based schedulers have been proposed to provide real-time user, target, and access point (AP) association in distributed cell-free integrated sensing and communication systems. In a typical approach, a graph neural network (GNN), trained on labels from a mixed-integer linear program, maps lightweight per-AP statistics to decisions on AP clustering, user and target scheduling, and mode selection in one forward pass. Such solutions assume that hard constraints, enforced only as soft training penalties, hold at inference, and that the self-reported statistics are truthful. Using our ASSENT algorithm as an example, we find that despite high $F_1$ scores, many solutions violate at least one hard constraint, demonstrating that high prediction accuracy does not ensure joint feasibility. Projecting the GNN output onto a feasible solution restores constraint satisfaction with low utility loss, even with a simple greedy repair procedure. We further show that feasibility alone does not guarantee robustness to false data injection attacks. A single malicious AP that reports false information cannot substantially increase its user associations, but can greatly increase the rate of infeasible solutions. The effect of such attacks depends on the type of information being falsified. Misreporting information that affects the objective can largely be mitigated through feasibility projection, whereas falsifying information that affects the constraints cannot. The latter can, however, be detected using a low-complexity cross-AP consistency check. These results show that learned ISAC schedulers should be evaluated using constraint-aware feasibility metrics in addition to conventional accuracy measures.
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Networking · Cross-listed from cs.CR

SecDT: A Profile-Based Security Layer for TRDP Communications

Erlantz Alonso, Igor Lopez and Jasone Astorga

The Train Real-time Data Protocol (TRDP) is widely used on rolling stock but it provides limited native support for cryptographic protection. Furthermore, the multicast traffic profile used in TRDP Process Data to exchange critical information between onboard subsystems makes the introduction of cryptographic protection a challenge. This paper presents a lightweight security layer for secure TRDP communication that implements a number of security profiles built around modern cryptographic algorithms. This additional layer relies on an On-board Key Management System (OKMS) for both security profile negotiation, dynamic key distribution and key lifecycle management. The security profiles allow for cryptographic agility and flexibility, ranging from simple authentication to Authenticated Encryption with Associated Data (AEAD) algorithms. The security profile negotiation procedure guarantees all TRDP End Devices (ED) on a common Communication ID (ComID) share the same security profile and can therefore process each other's messages. A prototype implementation based on mbedTLS and Arm Platform Security Architecture (PSA) was developed and evaluated. Experimental results demonstrate manageable overhead, suitable for the real-time and time-sensitive communication found on rolling stock.
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Networking

Efficient Co-simulator Integration with Application to Smart Grids

Talha Ibn Aziz, Jihoon Og, Ioanis Nikolaidis et al. (+1 more)

The evolution of the electric power grid towards a "smart grid" is an example of an emerging large-scale cyber-physical system. To study the potential of cyberattacks or other sources of extreme events, high-fidelity co-simulation is the only realistic strategy. In this work, we demonstrate a practical and high-performance co-simulation strategy for smart grids, which provides lessons about co-simulation integration that can be carried over to other cyber-physical systems. Specifically, we present a co-simulation platform based on the Mosaik framework that integrates several federates including OpenDSS for power flow, a refined NS-3 for communication networks, and custom Python-based simulators for on-load tap changer control, distributed state estimation, and data collection. We compare synchronization strategies – exhaustive lock-step time advancement versus an optimized event-driven approach that exploits lower-bound time stamps (LBTS) and next-event prediction – and introduce targeted refinements to NS-3 event handling and relevance filtering of internal events. Performance is evaluated on two standard IEEE benchmark systems: the 13-node test feeder with tap-changer voltage regulation and a large-scale 33-bus system augmented with 32 European low-voltage feeders (1,793 nodes total) performing distributed state estimation with thousands of phasors and smart meters. Experimental results demonstrate that the refined event-based synchronization that uses LBTS-based NS-3 event filtering reduces execution time by up to 50% (and more than 4$\times$ in smaller scenarios) compared to naïve lock-step methods, while strictly preserving temporal correctness and simulation accuracy. We additionally summarize a domain ontology that standardizes multi-simulator configuration and entity mapping across power, communication, and control domains.
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All authors Talha Ibn Aziz, Jihoon Og, Ioanis Nikolaidis, Omid Ardakanian

Networking · Cross-listed from cs.CR

Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

Ali Akarma, Toqeer Ali Syed, Muhammad Khan et al. (+2 more)

As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough information to identify who sent them, which threatens the anonymity that safety-critical V2X applications assume and adds to existing concerns over adversarial ML, model poisoning, and backdoor attacks. We study server-side client identity inference from transmitted weight deltas using inertial (IMU) measurements, evaluated on the UCI Human Activity Recognition (HAR) benchmark as an accessible proxy for the IMU streams produced onboard connected vehicles. Across five attack classifiers and five non-IID partitions, an honest-but-curious server recovers client identity with near-perfect accuracy (approximately 1.000) from undefended updates, confirming a concrete identifiability risk. We then quantify the privacy-utility trade-off of a lightweight clip-then-noise defense by sweeping Gaussian noise (sigma in {0.00, 0.05, 0.10, 0.20, 0.50, 1.00}) at fixed clipping (C=1.0), and report formal (epsilon, delta)-DP budgets through Renyi accounting. A practical region (sigma in [0.1, 0.2]) drives attack accuracy to near-random while costing under 5% relative FL accuracy. Ensemble FL supplies complementary structural privacy with a 1/K anonymity-set bound and no noise penalty. Results are supported by cryptographic (SHA-256) train/evaluation gradient disjointness, three seeds, and a count-normalized attacker-advantage metric. We position HAR explicitly as a proxy and discuss what validation on true vehicular telemetry would require.
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All authors Ali Akarma, Toqeer Ali Syed, Muhammad Khan, Qurat-ul-ain Mastoi, Adeel Ahmad

Networking

Microcell Hot Spot and Smart Antennas Evaluation in WCDMA Macrocell System

Carlos H. M. Lima, Francisco R. P. Cavalcanti, Vicente A. de Sousa Jr. et al. (+1 more)

The purpose of this contribution is to analyze a hierarchical cell structure with a hot spot embedded in a WCDMA macrocellular system. Particular attention is paid to the impact of introducing smart antennas in this context. A system-level simulation approach is employed, in which we consider two main solutions to address hot-spot traffic: the placement of a microcell antenna or the adoption of smart antenna technology at the macrocell. Relative gains in signal-to-interference ratio (SIR) are then evaluated. Distinct scenarios are defined based on the proportion of users of different service classes. An evaluation of which solution is best suited to each scenario is then conducted.
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All authors Carlos H. M. Lima, Francisco R. P. Cavalcanti, Vicente A. de Sousa Jr., Emanuel B. Rodrigues

Networking

Integrating Wi-Fi into 3GPP 5G Network Slicing: An Experimental Prototype Study

Nelson Ion de Oliveira, Marília Costa Muniz, William M. C. do Nascimento et al. (+5 more)

Network Slicing (NS) is a fundamental pillar of 5G and beyond networks, enabling the provisioning of isolated, logical networks tailored to specific Quality of Service (QoS) requirements. While 3GPP standards comprehensively define slicing architectures over cellular access networks, the seamless integration of Non-3GPP technologies such as Wi-Fi into a unified slice instance remains an active area of investigation, particularly regarding empirical validation. This paper presents an end-to-end prototyping study that integrates 5G Standalone (SA) and Wi-Fi networks by adapting the Trusted Non-3GPP Gateway Function (TNGF) to extend NS to WLAN networks, enabling the unified management of Wi-Fi transmission resources. We implement a functional testbed leveraging an open-source 5G Core and an explicit Non-3GPP access to validate multi-Radio Access Technology (multi-RAT) slice operation. Our empirical results showcase the dynamic viability of multi-RAT slicing under varying bandwidth allocations and traffic steering policies, providing a concrete proof of concept for unified 3GPP and Non-3GPP service delivery.
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All authors Nelson Ion de Oliveira, Marília Costa Muniz, William M. C. do Nascimento, João Pedro Brasil, Victor Farias Monteiro, Sérgio Barros, Maykon Silva, Augusto Venâncio Neto e Vicente A. de Sousa Jr

Networking

Connectivity of HAPS-Based Solutions for Large-Scale Wireless Networks: A Percolation Theory Analysis

Hao Lin, Mustafa A Kishk and Mohamed-Slim Alouini

In the era of sixth-generation (6G) wireless communication, numerous applications are expected to be realized, including environmental monitoring, smart agriculture, remote education, security protection, and intelligent transportation systems. These scenarios require large-scale, continuous Internet services in forests, rivers, oceans, and road networks, to name a few, where optical cables are difficult to deploy. High-altitude platform stations (HAPSs) emerge as a promising solution, offering low-latency, high-capacity services while facilitating the establishment of vertical heterogeneous networks (vHetNets) in fiber-less areas. This article investigates three HAPS-based solutions, where HAPSs can serve wireless devices directly or via gateway (GW) networks: the HAPS-to-device (H2D) scheme, the HAPS-to-GW-to-device (H2G2D) scheme, and the hybrid scheme. Leveraging percolation theory, we study the feasibility of large-scale continuous Internet coverage, where the key performance indicator (KPI) is the percolation probability. We discuss the subcritical and supercritical cases in different coverage schemes, and prove that the phase transition from zero to nonzero percolation probability appears when increasing the HAPS density or GW density. Numerical results verify that the curve of the critical condition of the phase transition exists between the derived lower bound and upper bound, which can help reduce the upfront cost of HAPS-based vHetNet solutions.
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