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Computer Science Networking

cs.NI Watch Networking

Wednesday 16 September

Networking

Reliability-Guided Trusted Repeater Node Selection in QKD-Enabled Metro Optical Networks

Arup Kumar Marik, Basabdatta Palit and Sadananda Behera

Quantum Key Distribution (QKD) in optical networks can provide information-theoretic security but is limited by its operability range, thereby requiring repeater nodes for extended coverage. Existing works typically assume that all repeater nodes and their associated key management systems are fully trusted, overlooking the risks posed by software exploits and insider threats. To address this, in this work, we propose a Bayesian fusion-based model to quantify the trustworthiness of each node. We have formulated a reliability-guided Trusted Repeater Node (TRN) selection framework for metro optical networks, where each node is assigned a trust score. We have subsequently mapped this trust score to link weights, which in turn has been used for reliable path computation for TRN selection, using the Dijkstra algorithm. We have also ranked the nodes using a composite score combining eigenvector and betweenness centrality, to capture their topographical relevance in the network. Simulation results on a reference topology demonstrate that the proposed method achieves approximately 8.93% higher path coverage than traditional centrality-based approaches like degree centrality, using the same number (around ten) of TRNs, thereby supporting reliable and resilient TRN selection for QKD-enabled optical networks.
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Networking

Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study

Shenjia Ding, David Flynn, Paul Harvey et al. (+2 more)

Modern networks must support changing topologies, configurations, and performance objectives, motivating fast and reliable performance estimation. Network digital twins (NDTs) enable what-if analysis for performance estimation in such network scenarios, however, existing machine learning-based NDT approaches often rely on entire topology representations, which are inherently monolithic and lack reusability under topological or traffic changes in the network. This paper introduces a composable NDT approach that decomposes networks into subgraphs represented by reusable unit twins that capture subgraph structure, configuration and traffic behaviours. A lightweight composer aggregates unit twin combinations to create NDTs that predict per-route end-to-end latency through an overall topology. Evaluation across controlled synthetic topologies and diverse traffic scenarios, real-world Topology Zoo topologies, and a public NDT challenge dataset demonstrates that the composable NDTs achieve high in-distribution accuracy while remaining stable under out-of-distribution scenarios. Comparison with monolithic full topology NDTs demonstrates that our composable approach achieves reusability, while achieving comparable or superior accuracy.
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All authors Shenjia Ding, David Flynn, Paul Harvey, Takamichi Miyata, Sumiko Miyata

Networking · Cross-listed from cs.OS

Netkit: Specializing Linux Packet Delivery for Container Networks

Daniel Borkmann and Paul Chaignon

Cloud-native microservices architectures rely on network namespaces for isolation, with the overhead of container communications remaining a critical performance bottleneck. While colocating containers on the same host mitigates some of this overhead, it cannot match the performance of communication within a single network namespace. Existing solutions either require application rewrites or fail to support the full Linux network stack expected by containerized applications. In this paper, we present netkit, an eBPF-based datapath that specializes the Linux networking stack to eliminate redundant backlog queue traversals during network namespace transitions. netkit leverages eBPF to transparently redirect packets between namespaces, bypassing unnecessary buffering while preserving compatibility with existing container applications. Our implementation in the Linux kernel, integrated with minimal changes to the Cilium network plugin for Kubernetes, improves throughput by up to 37% and achieves parity between container-to-container and process-to-process communications, effectively closing the performance gap introduced by namespace isolation.
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Networking · Cross-listed from cs.MM

MoQSplat: Adaptive Progressive Streaming of 3D Gaussian Splatting via MoQ

Emanuele Artioli, Mohammadreza Ghafari, Md Tariqul Islam et al. (+3 more)

3D Gaussian Splatting (3DGS) enables photorealistic novel view synthesis, but transmitting gigabyte-scale scene data remains challenging for immersive applications. Traditional HTTP Adaptive Streaming over TCP introduces Head-of-Line (HOL) blocking and coarse segmenting ill-suited to fine-grained 3DGS delivery. We propose MoQSplat, which maps 3DGS content onto the Media over QUIC (MoQ) transport hierarchy. MoQSplat partitions scenes into spatial Tracks, clusters splats into semantically coherent Groups, and constructs progressive-quality Subgroups mapped to independent QUIC streams to eliminate connection-level HOL blocking. Using a stateless, subscriber-driven adaptation loop, clients dynamically request spatial regions and quality tiers based on six degrees of freedom (6-DoF) frustum visibility, distance, and foveal alignment. We evaluate the core components on a prototype implementation, showing that opacity-based pruning outperforms scale-based pruning for progressive delivery. The source code is available at https://github.com/emanuele-artioli/MoQSplat.
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All authors Emanuele Artioli, Mohammadreza Ghafari, Md Tariqul Islam, Farzad Tashtarian, Christian Rothenberg, Christian Timmerer

Networking

Jamming Detection in 5G/6G Networks: From O-RAN Concept to OCUDU Deployment

Marcin Hoffmann, Lukasz Kulacz, Osama Baldo et al. (+2 more)

RF jamming poses a severe threat to 5G/6G networks, increasing packet latency being especially disruption to mission-critical URLLC services. This paper introduces a proactive Jamming Detection xApp (JD-xApp) for the Open RAN architecture that detects attacks by monitoring the moving-average Block Error Rate (BLER) via the E2 interface. Upon detection, the algorithm overrides standard link adaptation, enforcing a robust upper ceiling on the Modulation and Coding Scheme (MCS) to stabilize latency. To counter O-RAN platform adoption challenges, we transition the framework to an open-source Centralized Unit/Distributed Unit implementation (OCUDU). Evaluated with high-end Keysight lab equipment including the UXM 5G Wireless Test Platform and the PROPSIM F64 channel emulator, the JD-xApp reduces the expected number of packet retransmission attempts by about 67.3%. Finally, the system's feasibility is validated via over-the-air deployment using the POWDER lab infrastructure.
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All authors Marcin Hoffmann, Lukasz Kulacz, Osama Baldo, Marcin Pakula, Balaji Raghothaman

Networking · Cross-listed from cs.CR

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

Rushabh Vipulkumar Patel, Dipo Dunsin, Mohammed Almaiah et al. (+1 more)

AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, automated testing unaffordable for the smaller organisations that need it most. This paper presents PentestChain, a ten-phase automated penetration testing framework that couples a curated, deterministic exploit map with a cost-aware AI cascade-a local Ollama model (qwen2.5-7b) first, then free-tier OpenRouter and Cerebras, with a rule-based fallback that always produces output-and exposes the full pipeline through a Model Context Protocol (MCP) server with eleven tools. We make three contributions. First, we treat US-dollar cost per engagement as a measured, first-class evaluation metric and show that a 7B-parameter local model, kept off the critical path by a deterministic backbone, sustains end-to-end operation at zero measured paid-API cost. Second, we analyse the attack surface that an MCP-exposed offensive engine introduces, grounding a four-position threat model in the 2025 MCP incident record (the CVE-2025-6514 remote-code-execution flaw in mcp-remote, the postmark-mcp supply-chain backdoor, and the tool-poisoning-rug-pull-line-jumping class), and contribute four mitigations. Third, we specify a reproducible, containerised evalua-tion protocol aligned with the standardised testbeds now expected at top-tier venues-AutoPenBench, a Cybench subset, and the PentestGPT 182-sub-task benchmark-with multi-trial statistics (more than 10 trials per configuration, pass-at-k, non-parametric significance tests and effect sizes) and direct, same testbed reproduction of the PentestGPT and PentestAgent baselines rather than citation of their published numbers. On the legacy targets measured to date, the framework detected 26 services, enriched 34 CVEs, produced
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All authors Rushabh Vipulkumar Patel, Dipo Dunsin, Mohammed Almaiah, Mohamed Chahine Ghanem

Networking · Cross-listed from cs.MM

SemABR: Measuring Video Semantic Fidelity with Multimodal LLMs for Adaptive Bitrate Streaming

Shiqi Xu, Soung Chang Liew and Yuyang Du

Conventional video metrics such as PSNR, SSIM, and VMAF measure visual distortion or perceptual quality, but they do not directly capture semantic preservation: whether compression retains a video's objects, actions, and temporal narrative. Existing Quality-of-Experience (QoE)-driven bitrate-selection and resource-allocation methods primarily aim to minimize rebuffering and bitrate switching while maximizing perceptual video quality, without explicitly considering semantic preservation. To address this gap, we introduce video semantic fidelity (SF), a metric that quantifies how well a compressed video preserves the semantic content of its source. An offline multimodal large language model (MLLM) generates structured descriptions of the reference and compressed versions of the video, and a separate text-only large language model (LLM) evaluates their semantic correspondence. The resulting content-dependent SF–bitrate profiles are cached and queried by the online bitrate selector without invoking MLLMs at runtime. Evaluations on three subjective QoE benchmarks show a consistent positive association between SF and mean opinion scores (MOS). A separate human semantic-rating study evaluates semantic preservation and shows that SF correlates more strongly with human judgments than conventional video metrics. We then embed these profiles into a 5G MEC-assisted video-on-demand (VoD) resource-allocation framework at the base station. When wireless resources cannot support high bitrate levels for all users, the framework uses the SF–bitrate profiles to select bitrate levels jointly across users and reduce the semantic loss caused by the required bitrate reductions. NS-3 simulations with the 5G NR module show that the proposed framework achieves higher average and worst-user SF than the evaluated baselines, with a widening advantage as the wireless resources available to each user decrease.
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Networking

The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling

Qiao Liao, Zhiyong Feng, Bin Wu et al. (+1 more)

A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point on that front at run time. We propose PrefDT, to the best of our knowledge the first preference-conditioned Decision Transformer for the problem of joint trajectory, association and offloading scheduling. Its idea comes from language modeling: we hand the model the desired trade-off as an input, such that a single model only needs to be trained once offline to return any desired point on the curve in one rollout. The fleet's state is summarized by attention pooling with a per-user bypass, so the scheduler keeps working when user reports are lost. The energy target is a running budget decremented by what the fleet actually spends. As a result, when wind or load pushes consumption off the plan, the policy can track the difference and hold its budget. Because no corpus of preference-labeled flights exists, we design a distillation pipeline and build the corpus by ourselves. In simulation against 26 method variants, PrefDT produces the best trade-off curve of any learned method and holds its energy budget to within 0.6% when propulsion cost rises by half in mid-flight.
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All authors Qiao Liao, Zhiyong Feng, Bin Wu, Guodong Fan

Networking

Human Exposure to Non-Ionizing Radiation from Indoor Distributed Antenna System: Shopping Mall Measurement Analysis

Júlia da L. A. Silva, Vicente A. de Sousa, Jr. et al. (+9 more)

It is crucial to monitor the levels of Non-Ionizing Radiation (NIR) to which the general population may be exposed and compare them to the limits defined in the current standards, in view of the rapid rise of communication services and the prospects of a connected society. A high number of people visits shopping malls and since these locations usually have several indoor antennas close to the public, it is therefore a kind of place that must be evaluated. Thus, this work presents measurements of the electric field in a shopping mall located in Natal, Brazil. We proposed a set of six measurement points, following two criteria: places with great the flow of people and the presence of one or more Distributed Antenna System (DAS), co-sited or not with WiFi access points. Results are presented and discussed in terms of the distance to DAS (conditions: near and far) and flow density of people in the mall (scenarios: low and high number of people). The highest peaks of electric field measured were 1.96 and 3.26 V/m, respectively corresponding to 5% and 8% of the limits defined by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (ANATEL).
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All authors Júlia da L. A. Silva, Vicente A. de Sousa, Jr., Marcio E. C. Rodrigues, Fred Sizenando Rossiter Pinheiro, Gutembergue Soares da Silva, Halysson B. Mendonça, Ricardo Q. de F. H. Silva, João V. L. da Silva, Fernanda E. S. Galdino, Vitor F. C. de Carvalho, Lucas I. C. Medeiros

Networking

Open-source emulation-based test environment to settle O-RAN-compliant trials

Ramon Fontes, Allan Martins, Vicente Sousa et al. (+11 more)

Experimental tools are a key factor in both academic and industrial research communities to create design evaluations of new networking technologies that involve troubleshooting or changing the planning of deployed networks. Physical Software-Defined Radio (SDR) experimental platforms enable a design solution for the quick prototyping of wireless communication systems. However, SDR-based experimental platforms incur high costs, which leads to scalability limitations in the experimental settings. Having said this, network simulators, emulators, and new testbeds have attracted increasing attention. Emulation-based research prototyping can be distinguished from real communication networks and SDR-based platforms by allowing a tradeoff between cost and flexibility. This paper examines the Mininet-RAN emulation tool, which, as well as Radio Access Network (RAN) modeling, provides a way to test Open RAN Intelligent Controller (RIC) services without the need to deploy an entire RAN infrastructure. The Mininet-RAN creates virtual network elements, such as hosts, L2/L3 devices, controllers, and links, by combining some of the best emulator features, hardware testbeds, and simulators. By running the current code of standard practice Unix/Linux network applications and network stack, the Mininet-RAN enables real-world network data traffic patterns to be delivered to the RIC, regarding the most significant aspect of the dynamic generation of wireless system's KPIs. We provide the basic code of Mininet-RAN for the first two O-RAN Alliance-defined use cases involving V2X and UAV. The xApps are being implemented in O-RAN SC near-RT RIC, with Mininet-RAN which provides a closed-loop validation environment.
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All authors Ramon Fontes, Allan Martins, Vicente Sousa, Kaio Dantas, Lucas Medeiros, Pedro Alves, Marcelo Fernandes, Iago Rego, Eduardo Aranha, Vinícius Filho, Mateus Goldbarg, Wysterlanya Barros, Roger Immich, Augusto V. Neto

Tuesday 15 September

Networking · Cross-listed from eess.SP

Integrated Sensing and Communications: A Tutorial on Mathematical Foundations, Signal Processing and System Design for 6G

Sabit Ekin

Integrated sensing and communications (ISAC) is a cornerstone of sixth-generation (6G) wireless networks: a single waveform, aperture, and hardware platform simultaneously conveys information and senses the physical environment. Its literature, however, is spread across radar signal processing, estimation and information theory, and wireless communications, which makes it difficult for a newcomer to assemble the full mathematical picture. This tutorial fills that gap. Starting from complex-baseband signal models, we build the required foundations step by step, with self-contained derivations. We develop the sensing toolkit (matched filter, ambiguity function, maximum-likelihood estimation, Fisher information, the Cramér-Rao bound (CRB), and Neyman-Pearson detection) and the communication toolkit (Shannon and MIMO capacity, water-filling, and multiuser precoding), and then unify them into the fundamental limits of ISAC: the CRB-rate region, the capacity-distortion function, and the deterministic-random tradeoff. On these foundations we treat waveform design (OFDM radar with two-dimensional FFT processing, OTFS, and jointly optimized waveforms), transmit beamforming as explicit optimization, including semidefinite relaxation, and receiver-side estimation and detection. We then survey the architectures that scale ISAC to the network level (millimeter-wave and terahertz operation, reconfigurable intelligent surfaces, and cell-free networks), together with applications, standardization, and security. A worked MIMO-OFDM design example with concrete numerology ties the mathematics together. Throughout, notation is unified and tabulated, key results are derived rather than merely cited, and seminal contributions are placed in context, so that the reader finishes with both the mathematical fluency and the literature map required to begin research in ISAC.
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Networking

Analysis of Measurements in a Microwave Oven with High Level of Non-Ionizing Radiation (NIR) Before and After Repair

Júlia da L. A. Silva, Vicente A. de Sousa Jr., Marcio E. C. Rodrigues et al. (+5 more)

Non-ionizing radiation is the subject of several applications, such as the microwave oven, which is very popular. Its use is characterized by direct user operation and reduced distances between users and the NIR source. Therefore, the level of human exposure in this scenario deserves attention as it is a potential cause of biological effects. In this sense, this paper presents the measurement of a microwave oven with a high level of NIR (above the limit defined by the standard) before and after two types of suggested repairs: the use of epoxy putty (Repair 1) and the replacement of the external surface of the oven (Repair 2). The definitions of measurement methodology, based on FDA and INMETRO standards, are presented and discussed. Finally, we describe an analysis of the efficiency of these repair methods aiming at the safety operation.
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All authors Júlia da L. A. Silva, Vicente A. de Sousa Jr., Marcio E. C. Rodrigues, Fred S. R. Pinheiro, Gutembergue S. da Silva, Halysson B. Mendonça, Fernanda E. S. Galdino, Ricardo Q. de F. H. Silva

Networking · Cross-listed from cs.MA

Agentic Societies Need a Social Harness

Tapan Chugh, Vidushi Singh, Krish Jain et al. (+2 more)

An agentic society is a collection of AI agents that coordinate autonomously across trust boundaries, on behalf of different principals whose objectives may only partially align. We show experimentally that in agentic societies even honest, competent agents often fail to reach satisfactory outcomes with existing harnesses and messaging primitives, and that faulty or malicious agents can stall collaboration, influence outcomes, and pursue other harmful goals by exploiting vulnerabilities in communication (“speech”). We argue that agentic societies need a social harness for inter-agent interactions, in addition to each agent's personal harness, which manages its private context and communication with its principal. We propose a layered architecture for social harnesses which (i) prevents classes of failures outright, (ii) enables agents to detect invalid messages at runtime, and (iii) supports post-facto investigation and consequences, and highlight directions for future research to realize these capabilities.
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All authors Tapan Chugh, Vidushi Singh, Krish Jain, Arvind Krishnamurthy, Ratul Mahajan

Networking · Cross-listed from cs.CR

Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models

Ariel Duschanek-Myers, Thomas Welsh and Helmut Neukirchen

Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collecting this data on typical IoT devices often requires elevated operating system permissions and specialized drivers. Consequently, this paper investigates the feasibility of utilizing Received Signal Strength Indicator (RSSI) data to predict human locations. RSSI was selected because it is accessible even on devices with limited user permissions, and therefore is more applicable to a wider array of IoT devices. To bypass the tedious process of obtaining training data needed to train an RSSI-based model, an existing Wi-Fi pose prediction project was used in this research. However, that project assumed CSI data as input. Therefore, we investigate the feasibility of cross-domain inference, i.e., feeding RSSI data into that existing CSI-based model. We collected an RSSI dataset, synchronized with video ground-truth of a person moving within a room, to evaluate the model's performance. This evaluation confirmed that RSSI data can predict locations with approximately 80% confidence when human movement is present. This demonstrates that a model trained on CSI data can be used to evaluate low-granularity RSSI data consisting of decibel-milliwatt (dBm) values to roughly locate people in the collection space. These results imply that a wide range of IoT devices can be used for privacy invasion in Wi-Fi-dense environments.
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Networking

Agentic RDZ: Autonomous Zone Management with AI Agents and an FR3 Coexistence Use Case

Minh Dat Nguyen, Gabriele Gemmi, Tamerlan Aghayev et al. (+3 more)

Radio Dynamic Zones (RDZs) allow wireless experiments to operate outside conventional spectrum regulations while continuously guaranteeing protection for incumbent users. Existing RDZ prototypes automate this task procedurally, through handcrafted rules and predefined workflows, and become brittle when experiments encounter hardware impairments, user workflows and devices, or interference mechanisms not anticipated at design time. This paper introduces the agentic RDZ (A-RDZ), which, to the best of our knowledge, is the first RDZ realization in which agents use Large Language Models (LLMs) to perform spectrum management, experiment management, policy interpretation, and zone orchestration. Built on the GENESIS agentic framework, the architecture pairs autonomous reasoning with a deterministic policy gate and near-real-time (near-RT) reflexes, so that agents can improve outcomes but never weaken the zone's protection guarantee. We validate the A-RDZ on a hardware-in-the-loop Frequency Range 3 (FR3) (7.125-24.25 GHz) Open Radio Access Network (O-RAN) testbed in which a 5G New Radio (NR) experiment coexists with an emulated Fixed Satellite Service (FSS) earth-station incumbent. In an end-to-end use case, the monitoring agent detects an emission violation from live spectrum evidence, the orchestrator selects a mitigation that restores the interference budget while keeping the experiment running, and the action is applied and verified through the O-RAN control plane. We report the detection-to-mitigation latency decomposition and discuss the practical limits of agentic operation, including non-deterministic reasoning and decision-to-action translation.
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All authors Minh Dat Nguyen, Gabriele Gemmi, Tamerlan Aghayev, Paolo Testolina, Michele Polese, Tommaso Melodia

Networking · Cross-listed from cs.AR

Budgeted Express-Mesh: Traffic-Aware Link Placement and Deadlock-Free Adaptive Routing

Li Cao and Jingyuan Ma

We present Budgeted Express-Mesh, a topology-routing co-design that adds a small number of traffic-aware express links under a fixed wire budget. An ASPL-based greedy placement is refined by simulation-guided annealing, while packets use committed top-K routes selected from delayed express-link congestion and reservation signals. Across four synthetic workloads, optimized placements consistently improve high-load throughput over Mesh and random placement, and annealing further improves Greedy. The gains persist under delayed quantized congestion information, longer express-link latency, multi-flit packets, and a 16-by-16 heterogeneous workload.
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Networking

Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification

Igor Cherepanov, David Sessler, Alex Ulmer et al. (+2 more)

Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learns semantically meaningful and trustworthy patterns rather than exploiting spurious correlations. Conventional evaluation practices predominantly assess predictive performance. Consequently, whether the model relies on semantically meaningful patterns remains unknown. To address these challenges, we adapt the knowledge generation framework for network traffic classification. The adapted framework combines data, ML models, explainability, visualization, and expert reasoning to support the iterative exploration, verification, and refinement of model behavior and data preprocessing. The framework is grounded in findings from the literature, benchmark dataset analyses, practical experience with XAI-based traffic classification, and expert feedback, providing practical guidance for semantic model validation. By complementing predictive performance with semantic validation and human expertise, the proposed framework supports the development of network traffic classification models that are not only accurate but also robust and trustworthy.
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All authors Igor Cherepanov, David Sessler, Alex Ulmer, Thorsten May, Jörn Kohlhammer

Networking

AeroLat: Channel-Aware Latent Space Semantic Communication for Decentralized UAV Swarms

Rajdeep Ghosh, Goparaju Venkata Seshachala Sree Vatsava and Sudip Misra

Communication in latent space offers an intriguing alternative to symbolic messages for decentralized autonomous Unmanned Aerial Vehicle (UAV) swarms operating over bandwidth-constrained, time-varying wireless links. However, when homogeneous frozen models are prompted with discretized perceptual inputs, their broadcast states collapse toward the shared prompt template. In view of this, we propose AeroLat, a channel-aware latent semantic communication framework that uses evidence injection. The resulting latent states are then passed through an explicit communication model that encompasses bandwidth-limited serialization, additive noise and information staleness, which facilitates a joint assessment of communication fidelity and swarm-level coordination. Across multi-seed simulations, AeroLat provably remains resilient to codec choice, faults and increasing swarm size. It consistently reproduces the latent-swarm anomaly, while no-whitening controls recover the collapse. In particular, AeroLat is capable of reducing false similarity by 97.5%.
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Networking

A Mechanical Antenna for Improving Capacity Fairness in Dynamic Multi-Station Scenarios

Akihito Taya, Yuuki Nishiyama and Kaoru Sezaki

While indoor Internet of Things (IoT) and sensor networks increasingly rely on Wi-Fi access points (APs) to collect high-bandwidth data streams from multiple devices, conventional APs rely on static antenna deployments, whose fixed orientations are often suboptimal in dynamic propagation environments. To overcome this limitation, this paper proposes a mechanical Wi-Fi antenna control system that adaptively optimizes its 3D antenna orientation for dynamic multi-station scenarios. The proposed system autonomously actuates its physical antennas in response to perceived radio environments by combining state-specific black-box optimizers and capacity-based environment change detection. The evaluation results show that the proposed system improves channel capacity under dynamic station combinations, avoids unnecessary re-optimization under transient blockages, and triggers re-optimization after sustained environmental changes such as continuous blockage and device relocation.
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Networking

The Price of the Golden 6G Band: Evaluation of Beam Management Effort in FR3

Clémence Altmeyerhenzien, Ljiljana Simić and Marina Petrova

Frequency Range 3 (FR3), 7.125-24.25 GHz, regarded as the "golden band" for 6G networks, has less challenging propagation characteristics than FR2 while offering much wider bandwidth for high data rate applications than FR1. Reusing existing FR1 infrastructure for FR3 network deployments requires gNodeBs (gNBs) to employ antenna arrays and perform beam management, which has proven challenging at FR2. In this paper, we extensively study and characterize the beam management effort in an FR3 urban network, in terms of: beam alignment sensitivity, number of directional link opportunities, gNB handover and beam switch rates, and beam steering distance. Our results show that achieving a high and stable mobile throughput requires significant beam management effort across FR3 bands. While the beam tracking requirements are less stringent at the lower frequencies due to wider beams, the beam switching rate to a non-adjacent beam is relatively comparable at FR3 and FR2.
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Networking · Cross-listed from eess.SY

Joint Freshness and Age-Dispersion Control over Finite-State Markov Wireless Channels

Aresh Dadlani, Hina Tabassum, Muthukrishnan Senthil Kumar et al. (+1 more)

Age of information (AoI) has become a standard design objective for timely monitoring as it measures the freshness of the latest update at a receiver. AoI alone, however, is insufficient in goal-oriented applications where decisions depend on consecutive observations. Age dispersion complements AoI by measuring the generation-time separation between consecutive updates. In this paper, we study joint freshness and dispersion control for a generate-at-will status update link modeled as a finite-state Markov wireless channel. The objective is to minimize the long-run probability that either AoI or age dispersion exceeds a prescribed threshold under an average transmission rate constraint. For channel-dependent randomized transmission policies, we derive exact matrix expressions for the stationary joint distribution of AoI and dispersion. For reversible channels, mean dispersion is lower bounded by the reciprocal of the delivery throughput, and the difference is an explicit non-negative variance term for which we establish the exact equality condition. We then formulate the adaptive joint-threshold control problem as a constrained Markov decision process and prove that it admits an exact finite-state representation whose size scales linearly with the number of channel states and the threshold levels. Simulation results show that the controller reduces joint threshold violations by 37.3% relative to adaptive AoI-only control under the same transmission budget, while increasing mean AoI by 8.3%.
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All authors Aresh Dadlani, Hina Tabassum, Muthukrishnan Senthil Kumar, Masoumeh Moradian

Monday 14 September

Networking

Toward Mission-Critical ISAC: Reliable Energy-Aware Coordination in UAV Swarms

Masoud Shokrnezhad, Tarik Taleb and Amir Javadpour

Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable data links. However, active integrated sensing and communication (ISAC) operation imposes a dual energy burden on propulsion and transmission, threatening mission continuity through premature battery depletion. In this paper, we propose a two-tier UAV swarm architecture in which mission UAVs (MUAVs) execute cooperative ISAC for mobile aerial target tracking while dedicated charging UAVs (CUAVs), equipped with solar harvesting panels, replenish low-battery MUAVs via aerial UAV-to-UAV wireless power transfer (WPT). We formulate the joint minimization of the cooperative posterior Cramér-Rao bound (PCRB) over MUAV trajectories, per-slot sensing-communication time splits, WPT scheduling and admission, and CUAV rendezvous trajectories, subject to minimum uplink rate, dual-tier energy causality, WPT proximity, collision-avoidance, and speed constraints, yielding a non-convex mixed-integer program (MIP) that, to the best of our knowledge, is the first to jointly couple cooperative ISAC sensing quality with aerial WPT and dual-tier energy management. To solve it efficiently, we propose Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation (SCA), CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees. Simulation results demonstrate that RHAO reduces the mean PCRB by 16.8 times over a fixed-time-split baseline and 5.4 times over a static-trajectory scheme, while the aerial WPT subsystem sustains all MUAVs above the energy-critical threshold throughout the full mission horizon.
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Networking

Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching

Farnaz Niknia and Ping Wang

Wireless edge caching networks typically consist of many independent Base Stations (BSs), each facing its own request rate and content popularity profile. Training a Reinforcement Learning (RL) caching agent from scratch at every BS forces each agent to relearn, through slow trial and error, a decision problem that is structurally identical across the network. Meta-reinforcement learning removes this redundancy by learning a shared initialization that adapts to any BS in a few local updates; however, meta-training itself becomes the bottleneck at scale: the meta-gradient must be estimated from a small subset of BSs at each meta-iteration, and sampling this subset uniformly at random yields a high-variance estimate, an issue existing meta-RL caching frameworks leave unaddressed. This paper proposes a meta-reinforcement learning framework for caching across independent, non-overlapping BSs that directly targets this bottleneck. Each BS runs a local Proximal Policy Optimization (PPO) agent, formulated as a Semi-Markov Decision Process (SMDP) over content popularity, size, lifetime, and importance, while a shared meta-policy is learned via a Model-Agnostic Meta-Learning (MAML)-style loop. To scale meta-training and accelerate convergence, we introduce gradient-based clustering, which groups BSs by local gradient similarity and draws from every cluster, in proportion to its size, at each meta-iteration. We prove, via an Analysis of Variance (ANOVA)-style decomposition of gradient variance, that this strategy yields a strictly lower-variance meta-gradient estimator than uniform random sampling under BS heterogeneity.
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Networking · Cross-listed from quant-ph

Utility-Based Path Selection and Configuration in Quantum Networks via Layered Shortest Paths

Leonardo Bacciottini, Subhransu Maji, Don Towsley et al. (+1 more)

A path in a quantum network is a chain of repeaters that distributes entanglement between two users. Selecting a path requires balancing the rate and quality (e.g., fidelity) of the delivered entanglement, but these quantities, unlike standard routing metrics, compose non-additively. The problem is compounded by link-level configuration choices (e.g., distillation rounds or emitter brightness tuning), each trading rate against fidelity, so that a path's performance depends jointly on its route and its per-link settings. We cast this joint path selection and configuration problem as a shortest path computation on a layered graph whose layers track discretized end-to-end fidelity. A single run returns the full rate fidelity Pareto frontier, from which the path maximizing any nondecreasing utility function of rate and fidelity can be selected. We prove that for certain utility functions (including the secret key rate of BB84), the method is a fully polynomial time approximation scheme, returning a near-optimal path within a specified tolerance. We further characterize exactly when cheaper scalarization-based routing suffices: it is optimal for utility functions with convex fidelity profiles, but can be arbitrarily suboptimal otherwise (e.g., for step-like, sigmoidal utilities), whereas the layered method remains reliable in all cases.
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All authors Leonardo Bacciottini, Subhransu Maji, Don Towsley, Gayane Vardoyan

Networking

Understanding the oversubscription behaviour of DragonFly+ networks

Vlad-Adrian Ulmeanu, Costin Raiciu and Iulian-Ilie Drăcea

The Max-Host Dragonfly+ topology's original paper proves that there is a 2:1 worst-case oversubscription ratio in expectation for the permutation traffic pattern. We show that the proof only covers a subset of permutation patterns, specifically those in which all host pairs are in different groups, and for any receiver group there are at least two sending hosts in distinct groups. The proof contains two mistakes that cancel out to produce the correct result. Furthermore, the proof implicitly uses an important observation without a backing argument: traffic leaving an indirect group, now forced to follow min-cost paths to receiver groups, can still be split across almost all outgoing global links, in contrast with splitting traffic from a sender group that can naturally select any outgoing group. We prove that the 2:1 ratio holds in expectation for a larger set of patterns, including any permutation. We only need to know that any host sends and receives at most line rate traffic. This constraint can be altered to obtain approximations for expected FCT bounds on any pattern. We also attack the oversubscription problem without the expectation assumption, and find bounds that hold with high probability on permutations for small switch radixes. While in expectation the global layer wiring doesn't affect the oversubscription rate, we experimentally find that it matters in the general case. We find topologies that obtain visible speedups against the default global wiring from the original paper, under $7\%$ for radixes at most $8$.
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Networking · Cross-listed from cs.DC

Cnuas: A Software-Defined AI/HPC Rack-scale Emulation Platform and Hyperscale Data Center Facility Twin

Weqaar Janjua, Eoin OConnell and Mihai Penica

Modern AI and HPC systems integrate accelerators, high-speed networks, and management controllers at rack scale. Developing software for this infrastructure typically requires access to scarce, costly hardware, while software abstractions can obscure how workloads depend on resources across servers and accelerators. This paper presents Cnuas, an open-source, experimental rack-scale emulation platform whose baseline architecture follows the Open Compute Project (OCP) Open Rack v3 specifications. Through functional emulation, it supports experimentation, learning and software development within academic and industrial research and development, rather than matching the throughput or latency of physical hardware. Its web-based user interface visualizes racks, devices and their interconnections to help developers build a system-level mental model of the infrastructure supporting their workloads. At its core, CnuasNIC and CnuasSwitch implement a guest-visible remote direct memory access (RDMA) adapter and a host-resident hybrid software switch supporting both RoCEv2 and native InfiniBand. The platform also provides a dedicated AI/ML accelerator (GPU) peer fabric and OpenBMC-based rack management with executable power supply and battery backup firmware over RS-485. These components support the study of device, driver, and firmware interfaces on commodity hosts. The accelerator software stack remains an early research prototype, and facility modeling with OpenUSD is an exploratory extension. The paper presents the architecture, interfaces, and bounded prototype results as a basis for community collaboration across the core platform and its extensions.
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Networking · Cross-listed from cs.IT

Private Information Retrieval With Arbitrary Privacy Requirements: Introduction and Capacity Results

Mohamed Nomeir, Shreya Meel and Sennur Ulukus

In this paper, we introduce the problem of private information retrieval (PIR) under arbitrary privacy requirements, in a graph-based storage system. This formulation is motivated by the server storage limitations, abundance of data (messages) and heterogeneous data privacy requirements. Under the arbitrary privacy requirement, each message has to be retrieved privately from a pre-specified subset of servers, where the subset always includes the servers storing it. Thus, each server is associated with a privacy set, which pre-specifies the message indices that should be privately retrieved from it. This setting is a generalization of the classical PIR setting, where the required message index needs to be kept private from all servers, i.e., there, the privacy set of each server comprises all message indices. Our setting is also a bridge between the newly formulated local PIR (LPIR) setting and the classical PIR setting, where in the former, the privacy set is exactly the set of stored message indices. In this paper, we derive general lower and upper bounds on the PIR capacity for general graphs, under certain privacy requirements, that capture the essence of both LPIR and classical PIR. Then, we focus on path and cyclic storage graphs under these and more fine-grained settings, for which we derive capacity results for certain cases, and establish lower and upper bounds for others. Their low degree allows for a more in-depth understanding of the new privacy formulation and admits more privacy requirement settings compared to other simple graphs. Finally, we introduce a new graph structure, the pyramid storage graph, to model server storage. Although this graph has never been investigated in the literature in any PIR context, it enjoys a nice symmetric structure for message storage and replication patterns.
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Networking

Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence

Thai T. Vu, John Le, Tu N. Nguyen et al. (+3 more)

This paper proposes FREDI (Fair Resource Allocation for Edge Dual-Threshold Inference), a secure wireless edge-intelligence framework for event-triggered inference in a cooperative user equipment (UE)–edge server (ES)–cloud system. Each UE performs early-exit convolutional neural network (CNN) screening using dual confidence thresholds, while critical events are securely offloaded to an edge server for detailed classification. We formulate a proportionally-fair utility maximization problem that jointly optimizes UE–ES association, wireless and processing resources, and confidence thresholds. FREDI decomposes the problem into proportional-fair resource allocation and dual-threshold inference optimization. We prove that the detected-critical event set is set-monotone non-increasing in both thresholds, and exploit the finite empirical confidence domain for exact threshold optimization. An empirical resource–utility response envelope yields a computable global suboptimality bound and a sufficient condition for global optimality. By pre-eliminating infeasible UE–ES pairs and exactly projecting out bandwidth and transmit-power variables, the resource-allocation subproblem is reduced to a mixed-integer exponential-cone program solvable to the certified global optimality within a prescribed gap. Numerical results with early-exit MobileNetV2 and ShuffleNetV2 demonstrate near-perfect UE fairness with aggregate utility close to a Sum-Utility benchmark, reveal security-induced resource fragmentation, and demonstrate the Stage-A scalability from 6 to 144 UEs with median solving time below 0.1~s in the tested configurations.
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All authors Thai T. Vu, John Le, Tu N. Nguyen, Jun Shen, Quang Vinh Duong, Ha Nguyen

Networking

AI-Native Open RAN: A Roadmap from xApps and rApps to Autonomous Network Agents

Ryan Barker, Alireza Ebrahimi Dorcheh, Tolunay Seyfi et al. (+6 more)

Open Radio Access Networks (O-RAN) have emerged as a transformative paradigm for future wireless systems by introducing openness, virtualization, disaggregation, and programmable intelligence through the RAN Intelligent Controller (RIC). The availability of standardized interfaces and near-real-time control loops has created unprecedented opportunities for integrating artificial intelligence (AI) into radio access network management and optimization. Over the past several years, a broad range of AI techniques have been proposed to address key O-RAN challenges such as radio resource management, network slicing, traffic prediction, mobility management, interference mitigation, and spectrum sharing. Despite significant progress, existing solutions often remain task-specific, require extensive retraining, and exhibit limited generalization across deployment environments and network conditions. This paper presents a comprehensive review of AI-enabled O-RAN systems and provides a unifying perspective on the evolution of intelligence in wireless networks. We first examine the O-RAN architecture and the role of intelligence within near-real-time and non-real-time RIC frameworks. We then develop a taxonomy of AI approaches for O-RAN, covering machine learning, deep reinforcement learning (DRL), digital-twin-assisted optimization, and emerging foundation-model-based architectures.
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All authors Ryan Barker, Alireza Ebrahimi Dorcheh, Tolunay Seyfi, Mohammad Raihan Uddin, Alireza Mohammadhosseini, Julia Boone, Stephen Streit, Drew Schlesener, Fatemeh Afghah

Networking · Cross-listed from cs.LG

Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning

Zongjin Li, Shaohan Feng, Chunxi Yang et al. (+1 more)

Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-execution speeds, placement-dependent communication, and coupled task and container decisions. Workflows are naturally modeled as directed acyclic graphs (DAGs), but conventional vector- or matrix-based states do not fully capture their dependency topology. To better represent task urgency and structural relationships, we assign predicted sub-deadlines to tasks and use a multi-head graph attention network (GAT) to extract dependency information from the evolving DAGs. Based on these representations, we develop a Graph Attention-Driven Hierarchical Reinforcement Learning (GA-HRL) framework and model the scheduling process as an event-driven hierarchical semi-Markov decision process (SMDP). Workflow arrivals and task completions trigger scheduling events. At each scheduling event, the Task Scheduling (TS) agent first processes the currently ready tasks by assigning them to admissible existing containers or requesting new ones. The requested containers are then processed by the Container Scheduling (CS) agent for host placement before the environment advances. The two agents are trained alternately using separate Proximal Policy Optimization (PPO). Experiments on the 2018 Alibaba cluster trace show that GA-HRL maintains competitive workflow success rate and, in settings where success is comparable, generally achieves higher container utilization and lower energy consumption. Under the largest speed variation, it trades a small success-rate margin for substantially lower energy. Simulation code is available at: https://github.com/zongjin130/GA-HRL.
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All authors Zongjin Li, Shaohan Feng, Chunxi Yang, Wenbo Wang