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Computer Science Networking

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Thursday 1 October

Networking

DRL-driven RAN Slicing Management: A V2X-oriented Approach In Multi-service Scenarios

Daniel E. Garcia-Fernandez, Pablo Vera-Soto, Sergio Fortes et al. (+6 more)

The integration of Vehicle-to-Everything (V2X) communications is driving a profound transformation in vehicular connectivity, expected to significantly enhance traffic efficiency and safety. However, the stringent requirements of V2X services, particularly ultra-low latency and high reliability, present significant technical challenges. 5G's Network Slicing emerges as a key enabler by providing tailored virtual networks that ensure isolation and adaptability for heterogeneous services. This work proposes an intelligent Radio Access Network (RAN) slicing management framework specifically designed for scenarios where safety-critical V2X and high-capacity eMBB slices coexist. In such complex environments, harmonizing conflicting traffic requirements demands continuous, data-driven optimization. To achieve this, the proposed framework leverages an advanced Deep Reinforcement Learning (DRL) approach which dynamically optimizes resource allocation in real time. The framework is empirically validated on a real 5G Standalone (SA) network, where experimental results demonstrate that the DRL-driven approach successfully balances both objectives, outperforming traditional static and proportional allocation strategies by minimizing SLA violations while ensuring high resource utilization for eMBB slices.
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All authors Daniel E. Garcia-Fernandez, Pablo Vera-Soto, Sergio Fortes, M. Martinez, I. de-la-Bandera, M. L. Luque, A. Mendo, J. Ramiro, Raquel Barco

Networking · Cross-listed from cs.DC

GPU-Initiated Communication: Dissecting Down to the Bone

Javid Baydamirli, Ismayil Ismayilov, Kaan Oktay et al. (+1 more)

GPU-initiated communication lets GPU threads post RDMA operations directly to the NIC. It underpins NVSHMEM, NCCL GIN, and DeepEP, which serve the fine-grained, latency-critical communication of Mixture-of-Experts (MoE) models, yet its performance characteristics and optimizations remain scarcely documented beyond source code, and library comparisons fail to separate the costs of the hardware mechanism from those of the library around it. This paper dissects GPU-initiated communication at the GPU-NIC boundary. We first detail the GPU-side network path: queue placement, work-request construction, doorbell ordering, and completion semantics. We then introduce mini-gda and mini-proxy, minimal transports for the GPU and CPU-proxy submission paths, and measure them alongside NVSHMEM IBGDA, NCCL GIN, DeepEP, UCCL-EP, MSCCL++, and fabric-lib on NVIDIA H100, H200, B200, and GB200 platforms. A minimal GPU path issues an operation in 0.7 $\mu$s and completes in 4.0 $\mu$s; libraries add up to 4.6 $\mu$s of issue time through queue management, memory ordering, and completion scope, and issue time scales with the SM clock. A tuned CPU proxy matches or beats the GPU path at idle, at the cost of a dedicated core whose operating state sets its latency and throughput. On either path, sharing a queue with bulk traffic raises latency by one to three orders of magnitude. Reaching the 260 M msg/s ceiling of our InfiniBand platform requires doorbell batching and queue parallelism, and both have resource costs: communication code can reduce GPU block residency even when unused, and all-to-all traffic loses 59% of its NIC message rate at about 3,000 active connections. The submission path alone therefore does not predict communication performance. Our experiment code and results are available at https://github.com/ParCoreLab/Dissecting-GPU-Communication-Experiments.
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All authors Javid Baydamirli, Ismayil Ismayilov, Kaan Oktay, Didem Unat

Networking

Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

Emilio Paolini, Andrea Pinto, Flavio Esposito et al. (+1 more)

Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped in this way, federated LLM traffic becomes a suitable candidate for selectively provisioned optical connectivity, where high-capacity paths can be established during aggregate-transfer windows and released between learning rounds. The resulting architecture combines the flexibility of packet-based mobile access with dynamically provisioned optical capacity, illustrating a broader approach for coordinating distributed AI workloads across programmable access and transport networks.
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All authors Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi

Networking · Cross-listed from eess.SP

Physics-Guided Bayesian Optimization for High-Dimensional Mixed-Variable MIMO Base Station Design

Koki Kanzaki and Koya Sato

This paper proposes a physics-guided Bayesian optimization for high-dimensional mixed-variable multiple-input and multiple-output (MIMO) base station (BS) design. The considered problem jointly selects a subset of candidate sites for BS deployment and optimizes the azimuth angles, downtilt angles, and transmit power spectral densities of the BSs, while each configuration is evaluated using computationally expensive site-specific ray tracing. To efficiently optimize the system configuration, the proposed method constructs a low-cost physics-based proxy from precomputed propagation information. The proxy-estimated communication coverage is used as the Gaussian process (GP) prior mean, and a residual GP with three-dimensional physical features learns the discrepancy between the proxy and full evaluations. Ray-tracing-based evaluations in two urban scenarios show that the proposed method achieves up to approximately 15 percentage points higher coverage than conventional and high-dimensional optimization baselines under the same evaluation budget.
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Wednesday 30 September

Networking · Cross-listed from eess.SP

ARCTAN: Arbitrary RF Containment Using Tactical Aerial Networks and Differentiable Ray Tracing

Samuel Rivera, Zhihui Gao, Yiming Li et al. (+1 more)

Aerial base stations (ABSs) can rapidly establish connectivity in ad hoc, infrastructure-deprived environments, but their broadcast, line-of-sight transmissions leak far beyond the intended service area, exposing communications to passive eavesdropping and interference. Prior physical layer defenses based on cooperative jamming typically assume known eavesdropper locations, simplified statistical channels, or continuously repositioned jammers. We instead pose the problem as a radio frequency (RF) containment: confining usable signal to a user-defined, arbitrarily-shaped target zone while denying it elsewhere independent of eavesdropper location. We present ARCTAN, a gradient-based optimization framework that jointly optimizes the position, orientation, and transmit power of stationary ABSs and cooperative jammers (CJs) by backpropagating through site-specific, differentiable 3D ray traced channels. Evaluated in a high-fidelity digital twin across three target zone geometries, ARCTAN achieves a mean in-zone SINR of approximately 10 dB while reducing mean out-of-zone SINR from 13-16 dB to -3-5 dB, and suppressing signal-leakage ratios from over 93% to below 46% requiring at most 10 of 12 candidate CJs.
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All authors Samuel Rivera, Zhihui Gao, Yiming Li, Tingjun Chen

Networking

Resource-Efficient Semantic Communication for Heterogeneous Agentic Teams

Farhad Rezazadeh, Hatim Chergui, Lingjia Liu et al. (+1 more)

Teams of autonomous agents, including large language model (LLM) agents, must coordinate over scarce and unreliable wireless links. We propose goal-oriented semantic communication (GOSC), a closed-loop co-design that jointly decides what each agent sends, when it sends it, and how reliably it is transmitted, based on each message's value to the team task. An edge broadcast of the team's common knowledge closes the loop by updating these values. We prove that a message is sent only if its value exceeds the cost of delivering it and that more valuable messages receive more robust transmission rates, and we show that the scheduler solves each scheduling step exactly whenever the radio budget is not saturated, which held in 95% of scheduling decisions. In search-and-rescue missions validated on unseen scenarios, GOSC meets the same mission targets as carefully tuned periodic semantic schemes with 1.2–8.5 times fewer uplink channel uses. In most settings, this advantage persists with realistic packet overheads, reaching 16.6 times fewer uplink channel uses and 13.8 times lower cost when downlink costs are included; in the rescue task, it also persists when all agents share one uplink. Rough value estimates suffice, whereas values that ignore message content can fail. With three different LLMs, GOSC uses 3.2–3.5 times fewer channel uses, while completion-time gains depend on the model.
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All authors Farhad Rezazadeh, Hatim Chergui, Lingjia Liu, Merouane Debbah

Networking

Spatio-Temporal Wireless-Optical Planning for Multi-UAV Networks

Binglei Wang, Huiru Ao, Fan Yang et al. (+3 more)

Multi-unmanned aerial vehicle (UAV) networks in urban low-altitude environments couple UAV mobility, wireless access, and optical backhaul resources. Existing path-planning methods optimize flight distance or wireless signal quality, but can still concentrate traffic on shared optical backhaul links. We present Spatio-Temporal Wireless-Optical (STWO) planner, a backhaul-aware path-planning algorithm that jointly considers flight distance, wireless link quality, and time-varying optical-link offered-load ratio. STWO updates backhaul occupancy during sequential multi-UAV planning, allowing later UAVs to avoid congested optical paths while maintaining wireless connectivity. Experiments show that STWO reduces peak optical-link offered-load ratio by up to 56.4% and congestion ratio by up to 72.6% under dense UAV deployment, demonstrating the importance of wireless-optical awareness for reliable multi-UAV transmission.
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All authors Binglei Wang, Huiru Ao, Fan Yang, Zhenjie Zhou, Zhonghua Peng, Jialong Li

Networking

MoSE: Mode-Switching Expander for Mixed LLM Training and Inference

Fan Yang, Ying Zhou, Binglei Wang et al. (+2 more)

AI clusters increasingly run large language model (LLM) inference and training on the same fabric. Prefill-decode (P-D) disaggregation creates key-value (KV) cache transfers between prefill and decode groups, whereas training collectives and all-to-all traffic benefit from near-uniform global connectivity. A static sparse topology can therefore be poorly matched to one of the two traffic patterns. We present Mode-Switching Expander (MoSE), a reconfigurable expander that treats topology design as a fixed-degree edge-allocation problem. MoSE reallocates the same sparse edge budget toward direct P-D connectivity in inference-heavy modes and restores a uniform random regular expander in training-heavy modes. We evaluate MoSE using a 1024-group flow-level topology model, shortest-path routing, and two mixed workloads. Across 20 seeds, MoSE reduces average and 95th-percentile (P95) load-aware KV communication cost by 90.8% and 91.9% relative to Static-Training in the inference-heavy mode. In the training-heavy mode, it reduces average and P95 training communication cost by 22.7% and 27.6% relative to stale Static-Inference. These results show that coarse-grained topology switching can support both workload modes without additional ports or routing changes.
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All authors Fan Yang, Ying Zhou, Binglei Wang, Zhenjie Zhou, Jialong Li

Networking

Can LLMs help find Ambiguities in Protocol Specifications?

Ziyue Dang, Sixu Tan, Atharva Nevasekar et al. (+3 more)

Internet protocol specifications written in RFCs are subject to ambiguities and multiple interpretations that can cause interoperability failure. While these have presumably cleared up after years of experience, such ambiguities can bedevil the adoption of newer protocols like 5G. The 5G specifications pair a formal message syntax (ASN.1) with message-handling procedures written in natural language. This creates semantic underspecification: a syntactically valid message can reach a state whose procedures never say how to handle it, so standard-compliant implementations diverge. We frame this as a gap or a fork in a partially specified communicating state machine, and present SpecLens, which puts that view in front of a language model as a scaffold. Stronger models do not remove the need for it: they broaden the search without disciplining it, and fewer than half their findings survive inspection. Across 36 procedures from six 3GPP and O-RAN protocols, experts accept 185 of 197 SpecLens findings, and 60 drive observable divergence between the OpenAirInterface and srsRAN implementations under differential test. While we use 5G as a canonical example of a newer protocol, we also show ambiguity results for the more mature DNS protocol.
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All authors Ziyue Dang, Sixu Tan, Atharva Nevasekar, Zhaowei Tan, George Varghese, Songwu Lu

Tuesday 29 September

Networking

Enabling Efficient Client Selection in FL-as-a-Service for Multi-Application based Society 5.0

Prachi Nandi, Sonakshi Satpathy, Timam Ghosh et al. (+1 more)

The rapid development of the Internet of Things (IoT) has led to the generation of vast amounts of data from sensors, prompting the need for advanced learning models to analyze this data for personalized services. Federated learning (FL) emerges as a solution, offering decentralized learning that preserves user privacy by building models on local devices and sharing only aggregated insights. This paper explores FL-as-a-service (FLaaS) in IoT, highlighting its potential for collaborative learning across applications while addressing challenges like security, privacy, and optimizing hierarchical architectures for efficient model convergence and accuracy. To enhance the effectiveness of FL in IoT environments, this study focuses on selecting optimal nodes for model processing through the evaluation of parameters such as delay, energy consumption, and link status. The proposed method aims to identify suitable client nodes for model training. Performance evaluation is conducted using a Human Activity Recognition dataset under simulated IoT network conditions. The proposed approach is compared against randomized and Q-learning based client selection strategies. Experimental results shows improvements in delay and energy efficiency while maintaining communication performance. The findings highlight the potential of efficient client selection mechanisms for enhancing FLaaS in dynamic IoT ecosystems and supporting future intelligent services in Society 5.0.
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All authors Prachi Nandi, Sonakshi Satpathy, Timam Ghosh, Arijit Roy

Networking

Priority-Aware Routing for Quantum Networks:Integrating Coherence-Time Constraints into Scheduling

Sadhgun Ram Dasi and Aswath Babu H

Quantum networks face a fundamental challenge absent in classical networks; finite memory coherence times mean that queuing delay directly degrades information quality, causing decoherence that has no classical analog. Existing routing protocols optimize for hop count or channel fidelity while treating scheduling delays as a secondary concern, leaving fidelity vulnerable to congestion-induced decoherence. We present a coherence-aware routing protocol that integrates decoherence constraints into path selection via a priority-modulated aging model derived analytically from Weighted Fair Queueing theory, where high-priority traffic receives compressed effective queue delay. We build a custom discrete-event simulator verified against NetSquid (across 104 states), and evaluate across two structurally diverse topology classes under identical physical parameters, isolating topology structure as the sole independent variable. In all experiments, our protocol is benchmarked against loss-only Dijkstra routing and FIFO scheduling as baselines. On topologies, P2 traffic achieves 96.08% fidelity under a 9x load increase with only 0.04 percentage point degradation, versus a 13.51 percentage point collapse for the loss-only baseline; P2 tail latency holds stable at 0.055 ms while Dijkstra degrades 5.5x. On Barabasi-Albert scale-free topologies, our protocol delivers a +4.12 percentage point fidelity advantage with 97.8% higher aggregate throughput at operational loads over the baseline, with a clearly characterized hub-saturation boundary at 160k QPS. FIFO scheduling provides negligible priority differentiation on both topology classes, providing strong evidence that coherence-aware routing-not scheduling alone is responsible for the observed QoS gains.
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Networking

Pricing IoT Data Delivered via LEO Satellites

Rahul Ramachandran and Suman Banerjee

IoT terminals served by LEO satellite constellations transmit data to passing satellites in discrete uplink windows. That data is delivered to buyers only when the satellite reaches a ground station. This store-and-forward structure makes the achievable price a discontinuous function of the delivery completeness threshold (a phenomenon we call a pricing cliff) and creates a geographic pricing asymmetry between polar and equatorial terminals that depends on constellation size. We present an integrated pricing model combining a full SGP4 orbital simulation of the deployed Kinéis constellation with a buffer flow model and a value decay framework with decay constants derived from operational decision-window timescales in the application literature and anchored to observed satellite imagery market prices. We show that polar and equatorial terminals are dominated by structurally different latency components requiring different infrastructure interventions, formally characterize the pricing cliff phenomenon and its commercial significance, and derive a geographic pricing premium that is exactly independent of the residual value assumption. Going from 1 to 25 satellites reduces the polar–equatorial premium by 25x, with diminishing returns beyond n=10; at full constellation size the premium falls below 1% of peak data value and remains below 1.5% across the full plausible range of buyer urgency.
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Networking

Sequence Models for Layer-3 Protocol Emulation

Alix Jeannerot, Petko Petkov and Alvaro Valcarce Rial

This article investigates whether Layer-3 radio-protocol behavior can be represented by compact sequence models suitable for deployment inside the RAN. We introduce the RRC sequence engine (RSE), a hybrid architecture in which a sequence model predicts protocol-dependent message structure while deterministic components retain control over security-sensitive or configured fields and over transport containers. Using NR protocol traces, we show that protocol-aware tokenization, cross-stack context, and explicit placeholders matter more than general-sequence model scale. A purpose-built 11M-parameter Mamba engine achieves 0.90 exact match on the evaluated next-generation Node B (gNB)-side test set with a median generation latency of 115 ms, sufficient for some timers of Layer-3, outperforming a fine-tuned 0.6B-parameter model. We discuss applications in testing, simulation, deployment specialization, and future trainable 6G control planes, as well as the validation, latency, robustness, and security challenges that remain before operational use.
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Networking

NetLexicon: Learning Discrete Behavioral Representations for Encrypted Web Traffic Analysis

Xiangyu Gao, Tong Li, Ziqiang Wang et al. (+9 more)

Encrypted Web traffic analysis requires effective representations of observable communication behavior. Existing pretraining methods often adapt NLP/CV objectives and sequence architectures, motivating learning objectives that capture traffic-specific interaction patterns. We present NetLexicon, a discrete pretraining framework that learns reusable behavioral states from unlabeled traffic. It converts contextual traffic windows into discrete states through vector quantization, constructing a compact traffic lexicon. We design two complementary pretraining objectives. State Transition Prediction (STP) forecasts subsequent sequence structure and packet features from observed history, while Statistical Feature Alignment (SFA) grounds learned states in window-level traffic statistics. Together, they guide the lexicon to capture recurring communication behaviors and their evolution. We evaluate NetLexicon on four benchmarks covering Web application identification, service type identification, and malware detection. NetLexicon improves Macro-F1 by up to 25.5 percentage points over the strongest baseline on each benchmark and reduces fine-tuning time per epoch by up to 23.6 times relative to the evaluated baselines. Further analysis shows that the learned discrete states capture recognizable patterns in packet size, timing, and data transfer. These results demonstrate that incorporating observable behavioral structure into pretraining supports effective, efficient, and interpretable representations for encrypted traffic analysis.
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All authors Xiangyu Gao, Tong Li, Ziqiang Wang, Yinchao Zhang, Rongbang Wu, Zhenxing Zhang, Jing Hu, Hanlin Huang, Xinle Du, Su Yao, Qi Li, Ke Xu

Networking

RingStitch: Demand-Aware Optical Stitching for Fragmented TPU Clusters

Huiru Ao, Fan Yang, Binglei Wang et al. (+2 more)

Large-scale AI training clusters increasingly use optical circuit switching (OCS) to reconfigure rack-level interconnects and create elastic accelerator slices. In multi-tenant TPU-style clusters, however, small and medium jobs often leave partial free capacity stranded inside racks. Although the aggregate free capacity may be sufficient for a new job, it cannot be used by local placement or coarse full-rack stitching. This paper presents RingStitch, an OCS-based defragmentation scheduler that turns fragmented rack capacity into schedulable resources. RingStitch follows a local-first policy, stitches compact cross-rack fragments only when needed, and orders the selected racks into a low-cost logical ring. Simulations on a TPU 8t-like SuperPod model show that RingStitch improves schedulability over local and full-rack baselines.
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All authors Huiru Ao, Fan Yang, Binglei Wang, Bo Liu, Jialong Li

Networking · Cross-listed from quant-ph

Optimal Entanglement Routing in Quantum Repeater Chains: Beyond Fixed Operation Order and Purification Schedule

Aikaterini Mandilara, Antonia Tsili, Dimitris Syvridis et al. (+1 more)

Entanglement routing establishes entangled pairs between distant nodes of a quantum network by purifying and swapping pairs generated on elementary links. Existing methods typically restrict the decision space along two axes: the operation order, often fixed to purify-then-swap (PtS), and the purification schedule of each link, often restricted to pumping. Focusing on linear repeater chains with finite link capacities, we relax both restrictions and study the maximization of the expected end-to-end throughput subject to a fidelity threshold under two quantum-noise models. We develop a unified capacity-aware optimization framework comprising an exact mixed-integer linear program (MILP) under PtS, instantiated with either pumping or general tree purification schedules, and an exact dynamic program (DP) over arbitrary operation orders. Under symmetric Pauli noise, we prove that pumping converges to a fidelity strictly below unity, imposing a capacity-independent feasibility bound and a maximum chain length on every pumping-based PtS method, whereas general tree schedules yield a capacity-dependent bound. We further prove that post-swap purification never increases the throughput, so a free operation order can only increase feasibility. Numerical evaluations confirm the bounds: tree schedules serve over 90% of the requests that pumping cannot at moderate fidelity thresholds; within the pumping class, a free operation order recovers much of this advantage; but once links use tree schedules, the tree-based MILP and the order-exact DP serve identical request sets throughout. Operation order and purification schedule are thus substitutes, with the purification schedule the dominant factor governing feasibility under symmetric Pauli noise.
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All authors Aikaterini Mandilara, Antonia Tsili, Dimitris Syvridis, Konstantinos Christodoulopoulos

Networking

Exponential Backoff: Meta-Stability and Implicit Admission Control

Richard Combes, Fabien Mathieu and Thomas Bonald

We analyze exponential backoff, an algorithm used to share a single communication channel in a distributed manner between several users, in a similar way as many networking standards such as 802.11. We demonstrate that this algorithm has a meta-stable behavior, in the sense that the system oscillates over long time-scales between meta-stable configurations where only a subset of the sources effectively access the channel. In other words, meta-stability creates a form of implicit admission control. We provide both theoretical tools and numerical experiments to understand this phenomenon further. Our experiments are fully reproducible and the code is publicly available as a Python package available at https://pypi.org/project/slotted-aloha-simulator/.
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Networking

DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioning

Shengjie Zhong, Zhongliang Zhao, Jingxuan Chen et al. (+4 more)

Uncrewed aerial vehicle base stations (UAV-BSs) are expected to cover traffic demand that shifts across space and time, yet most repositioning schemes either re-solve an optimization problem per slot or learn reactive policies without an explicit demand model. We cast demand-driven fleet repositioning as latent-space decision-time planning and propose DSWM, a decomposed spatio-temporal world model: an agentic controller that perceives the demand field through a rolling observation window, retains operational context in a latent recurrent state, reasons about candidate motions by imagined rollouts under an uncertainty penalty, and coordinates the fleet through replanned first actions. DSWM learns a recurrent state-space model shaped by an exponential-moving-average (EMA) based latent predictive objective with variance regularization. It attaches a differentiable service simulator that replays the association, probabilistic line-of-sight channel, and Shannon rate chain inside latent rollouts. Planning uses a cross-entropy method whose imagined demand is anchored on the current observation window with mixing coefficient $\rho=0.95$. On a unified pipeline over three real datasets (Milan CDR (call detail record), Shanghai Telecom, YJMob100K) and 14 methods including five reproduced IEEE baselines, DSWM attains weekday served ratios of 0.889, 0.908, and 0.898, ranking first among non-ablated configurations on every dataset. On Milan it improves over the strongest non-learning baseline (Greedy, 0.780) by 0.109, a margin that comes from decision-time use of observations rather than prediction accuracy.
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All authors Shengjie Zhong, Zhongliang Zhao, Jingxuan Chen, Xianbin Cao, Xinmei Qiang, Dapeng O. Wu, Tony Q. S. Quek

Networking

SCORAS-MoE: Joint Compression and Resource-Adaptive Deployment of MoE-VLMs in LEO Satellite Networks

Tong Quan, Yuanlong Wan, Huasen He et al. (+4 more)

Deploying large vision-language models (VLMs) onboard satellites enables onboard data processing and reduces raw data downlink. However, onboard inference faces two resource challenges. Limited onboard memory and energy require model compression and distributed deployment. Dynamic resource availability requires fast deployment decisions as illumination, battery levels, and communication conditions change. We present SCORAS-MoE, a joint compression and deployment framework for mixture-of-experts (MoE) VLMs in low Earth orbit (LEO) satellite networks. To address limited resources, SCORAS-MoE measures the perturbation of the routed MoE output caused by low-rank approximation, assigns higher ranks to more sensitive experts, and distributes compressed model shards across satellites for cooperative inference. The compressed models yield profiles of measured accuracy and inference energy. To adapt to dynamic resources, the online scheduler selects profile compositions and shard placements in each slot. For each candidate composition, it reduces placement to a minimum-cost assignment problem solved by the Hungarian algorithm, while enumerating the compositions yields the optimal deployment for the current-slot objective. Experiments on Qwen3-VL-30B-A3B-Instruct show that allocating ranks based on output perturbation is particularly effective under aggressive compression, with an absolute gain of $3.7\%$ in mean accuracy over uniform rank allocation when expert projections retain $30\%$ of their original parameters. The fixed-profile scheduler achieves higher throughput with fewer service switches and lower battery impact than the evaluated proximal policy optimization (PPO) and evolutionary baselines, with respective speedups of $8.7\times$ and $183.5\times$. Adaptive profile selection further improves the balance between service quality and energy use.
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All authors Tong Quan, Yuanlong Wan, Huasen He, Yunpeng Hou, Shuangwu Chen, Xiaofeng Jiang, Jian Yang

Networking · Cross-listed from cs.IT

On the Lack of Periodicity of Walker Satellite Constellation Routing Tables

Chang-Sik Choi and François Baccelli

In a referential that rotates with Earth, the dynamics of the configurations of satellites in a Delta Walker constellation can be analyzed as a dynamical system as a function of a translation on the torus. This paper extends this type of analysis to routing over a Walker constellation, leveraging fixed ground relays. It considers any deterministic, time-invariant routing rule on such a collection of satellites and relays. The main object of interest is the routing table associated with this rule, which specifies, for all source-destination pairs, a route made of satellites and relays between them, for instance the shortest, together with angular information on next hop at each step of the route, which is essential for beamforming in this context. The main result is that, for each such routing rule, the routing table inherits the dichotomy of the torus flow: when the ratio of the Earth-spin and satellite angular speeds is rational, the routing table process is periodic with the same period as the constellation. When it is irrational, the table is non periodic but admits ergodic long-run averages which can be computed as ensemble averages w.r.t. an explicit invariant measure. Simulations with mixed satellite–gateway greedy routing illustrate these findings in terms of both temporal and spectral properties of the Walker routing table time series. The paper also discusses how to define metrics that cope with such non periodic fluctuations when present. This is illustrated by a comparison of the round-trip times between two far away cities as offered by a Walker constellation using greedy routing on one side, and by the currently available terrestrial fiber on the other side. It is shown how ergodicity can be used to make this comparison between the time varying instantaneous round trip times of the constellation and the constant round trip time of the fiber network meaningful.
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Monday 28 September

Networking

Characterizing the eBPF-Based Data Plane for Multi-ClusterKubernetes: A Systematic Evaluation of Cilium Cluster Meshand KVStoreMesh

Simhadri Podala Narasimha

Kubernetes deployments increasingly span multiple clusters for reasons of scale, fault isolation, regulatory boundaries, and heterogeneous hardware placement, including GPU-dense clusters dedicated to AI and HPC workloads. Cilium Cluster Mesh, built on extended Berkeley Packet Filter (eBPF) technology, has emerged as a widely deployed mechanism for connecting such clusters into a single logical network without a dedicated multi-cluster gateway. Despite its adoption, the academic literature contains no systematic, reproducible evaluation of the eBPF-based multi-cluster data plane itself: existing work either benchmarks eBPF against iptables within a single cluster, or applies eBPF to cross-cluster monitoring rather than forwarding and identity propagation. The most detailed scale data available - a report of a clustermesh-apiserver failing under load at approximately 45,000 nodes across 256 clusters - comes from an industry engineering blog rather than a peer-reviewed source. This paper proposes a methodology to close that gap. We define five research questions covering latency, throughput, control-plane limits, churn, and GPU/RDMA sensitivity, and describe a testbed and eBPF-level instrumentation design. Lacking a live multi-cluster testbed for this submission, we report results from a calibrated analytical simulation of control-plane architectures fit to the production data point. These results are presented as falsifiable predictions intended for validation once the testbed is complete. This paper provides the motivation, experimental design, and preliminary simulation results to serve as a template for a full empirical study.
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Networking

People effects on IoT indoor wireless channel characterization

Millena Michely de Medeiros Campos, Mateus de Oliveira e Mattos, Rafael da Silva Macedo et al. (+3 more)

Wireless communication under 1 GHz is suitable for Internet of Things (IoT) applications due to larger coverage capability with less power consumption. Bearing in mind that people and elements contained in the environment can cause variations in the channel, this paper aims to evaluate the effect of the presence of people on a 900-MHz indoor narrowband wireless channel, as we characterize the small-scale phenomena. With the increase in the number of people, a greater variation in the communication channel was noticed, which is reflected in the parameters of the probability distributions used in the characterization of the random part of the signal. In addition, second-order statistics were used to analyze the data and an adherence test was applied to confirm the behavior of the signal in relation to the distributions.
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All authors Millena Michely de Medeiros Campos, Mateus de Oliveira e Mattos, Rafael da Silva Macedo, Alvaro Augusto Machado de Medeiros, Wellerson Viana de Oliveira, Vicente Angelo de Sousa Junior

Networking

Non-Ionizing Radiation Analysis in Close Proximity to Antenna Tower: A Case Study in Northeast Brazil

André B. F. Diniz, Vicente A. Souza Jr., Marcio E. C. Rodrigues et al. (+3 more)

While the amount of telecommunications services grows rapidly in the whole world, humans get potentially more exposed to Non-Ionizing Radiation (NIR) from a number of different sources. This paper has a number of goals, including monitoring radiation levels in an everyday situation, an investigation of how national and international regulations organs address NIR levels and a demonstration of scientific production trends regarding this topic. The work also presents a bibliometric study about the main scientific productions and trends related to NIR measurements, with focus on the field of Telecommunications. Another contribution is a case study in Natal, Brazil, where NIR levels were monitored in four distinct locations over a period of 24 hours each and compared to the current regulations. The result is a quantitative analysis of the amount of radiation that some populations might be exposed to in distinct moments of the day. Measurements were carried out in the proximity of an antenna spot, since it could represent a source of high NIR levels (a worst case of human exposure). This campaign differs from older studies by including a more intense usage of microwaves frequencies due to operating 4G and pre-5G systems in Brazil. Finally, a statistical study involving the measurements is conducted, concluding the analysis of how some population groups might be affected by NIR.
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All authors André B. F. Diniz, Vicente A. Souza Jr., Marcio E. C. Rodrigues, Halysson B. Mendonça, Gutembregue S. Silva, Fred S. R. Pinheiro

Networking · Cross-listed from physics.ins-det

Non-Ionizing Radiation Measurements for Trajectography Radars

J. Marcos Leal Barbosa Filho, Millena M. de M. Campos, Daniel L. Flor et al. (+4 more)

This work presents a Non-Ionizing Radiation (NIR) measurement campaign and proposes a specific measurement method for trajectography radars. This kind of radar has a high gain narrow beam antenna and emits a high power signal. Power density measurements from a C-band trajectography radar are carried out using bench equipment and a directional receiving antenna, instead of the commonly used isotropic probe. The measured power density levels are assessed for compliance test via comparison with the occupational and general public exposure limit levels of both the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (Anatel). The limit for the occupational public is respected everywhere, evidencing the safe operation of the studied radar. However, the limit for the general public is exceeded at a point next to the radar's antenna, showing that preventive measures are needed.
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All authors J. Marcos Leal Barbosa Filho, Millena M. de M. Campos, Daniel L. Flor, William S. Alves, Adaildo G. D'Assunção, Marcio E. C. Rodrigues, Vicente A. de Sousa Jr

Networking

Traffic Congestion Awareness and On-Demand Distribution in Vehicular Delay-Tolerant Networks in California I-210 Freeway

Xiaofei Liu and Milena Radenkovic

In vehicular networks under edge computing environments, vehicle-to-vehicle delay-tolerant networking (V-DTN) can disseminate congestion warnings to other vehicles via a store-carry-forward mechanism, helping them proactively choose suitable routes. However, most existing in-vehicle information dissemination methods rely on flooding or limited flooding strategies, broadcasting alerts across the entire network whenever congestion is detected. This leads to excessive redundant copies and consumes node cache space. To address this issue, this paper proposes a congestion-event- aware on-demand message dissemination mechanism. By considering the recurrence and duration of congestion, the mechanism suppresses broadcasts of short-lived, self-dissipating congestion events. Based on real-world PeMS data from California's I-210 corridor, we construct an I-210 Freeway Traffic Congestion Use Case. Experiments show that predicting congestion recurrence heavily relies on historical free-flow speed data of lanes. Without incorporating additional feature dimensions, the accuracy of modeling and comparing lane free-flow speeds across different day types outweighs the choice of machine learning models, and such patterns are difficult to reproduce in simulators. Meanwhile, congestion duration prediction serves effectively as the basis for dynamic-threshold on-demand dissemination in V-DTN, which demonstrate that machine learning-based congestion awareness combined with dynamic-threshold on-demand dissemination significantly reduces network resource consumption and network-layer congestion, enhances delivery reliability, and lowers delivery latency.
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Networking · Cross-listed from cs.LG

TORQUE: Optimizing What (not) to Quantize Before and After Rotation

Ran Ben Basat, Michael Mitzenmacher and Shay Vargaftik

Uniform random rotations are an effective preprocessing step for quantization: they make normalized coordinate distributions approximately Gaussian, enabling the use of codebooks optimized offline. We introduce TORQUE, a framework that improves on previous quantization works that use random rotations by jointly optimizing how many and which coordinates to preserve at high precision both before and after rotation, under a fixed overall expected bit budget. Intuitively, before rotation, preserving large input coordinates at high precision can reduce overall error by preventing the rotation from spreading their values across many coordinates. Likewise, after rotation, preserving a small fraction of the largest-magnitude coordinates at high precision allows the remaining values to be quantized more accurately using codebooks optimized offline for the resulting truncated Gaussian distribution. We derive a quantization error upper bound and prove that top-$k$ pre-rotation retention minimizes it for each $k$. This reduces the search over coordinate subsets to an optimization over $k$, enabling a fast optimizer that uses offline codebooks and parallel parameter selection for practical implementation. We demonstrate an improved tradeoff between reconstruction accuracy and storage cost through numerical evaluation under the Gaussian model and experiments on nearest-neighbor retrieval, KV-cache compression, and activation compression.
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Networking

MINT: Modeling GenAI Impact on Network Traffic

Andrew Nguyen, Samson Kempiak, Agrim Gupta et al. (+2 more)

Generative AI (GenAI) is becoming a mainstream network workload, yet packet-level simulators lack measure\-ment-driven GenAI traffic models. Currently researchers must approximate GenAI services using traditional sources such as file transfer and video streaming, limiting realistic network evaluation of scheduling and capacity planning. We present MINT, a measurement and modeling framework for GenAI network traffic. Using an isolated net\-work-namespace capture pipeline, we collect client-side traces from three LLM providers across four modalities, cloud and edge servers, and wired and wireless network access points. We find that GenAI modalities exhibit distinct upload/download asymmetry and burst structures that differ from traditional applications. MINT clusters and models these burst regimes and validate empirical burst timing distribution behavior in ns-3 with normalized Wasserstein distances of 2–25%. Our results also reveal that realistic packet bursts have significantly more variability than constant token generator models. MINT open-sources the first measurement-driven GenAI traffic model for packet-level network simulation.
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All authors Andrew Nguyen, Samson Kempiak, Agrim Gupta, Koushik Kar, Ish Kumar Jain

Networking

Energy-Driven Evaluation of Network Digital Twinning Applied to mmWave Beam Management

João Borges, Bruno Castro, Kleber Cardoso et al. (+2 more)

Network Digital Twins (NDTs) are important enablers of 6G and future networks. However, there is a lack of studies regarding practical aspects, such as the impact of simultaneously changing twinning rate, fidelity, and other NDT operational parameters. For instance, works often consider the impact of operational parameters in isolation or with physical twin (PTwin) implementations relying on simulations. Therefore, the main contribution of this work is to provide an in-depth study on the performance impacts of both twinning rate and fidelity, using PTwins that rely on measurements obtained from hardware. We also investigate the optimization of these two operational parameters to minimize energy consumption, exploring a Bayesian Optimization (BO) method and what-if analysis. In this work, the NDT models an indoor propagation environment to optimize beam management. The virtual twin (VTwin) was implemented with the Sionna ray tracing (RT) simulator and the PTwin with our in-house setup composed of customized Wi-Fi radios with 32 antenna elements operating at 60 GHz. The study reveals important aspects of wireless channel NDTs, suggesting that fidelity levels vary throughout the experiment and with the what-if difficulty. Moreover, the twinning rate can be adjusted using sample-efficient methods, such as BO, even at lower fidelity levels.
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All authors João Borges, Bruno Castro, Kleber Cardoso, Andrey Silva, Aldebaro Klautau

Networking

Resource versus Responsiveness: Benchmarking SDN Controller Runtimes for a Moving-Target-Defense Control Plane at Scale

Souhail Chakkour, Umesh Biswas and Charan Gudla

Network Moving Target Defense (MTD) built on Software-Defined Networking (SDN) continuously rotates host-facing addresses to invalidate an attacker's reconnaissance. Each rotation creates a burst of control-plane mutations, while new connections may simultaneously require reactive flow installation. Yet SDN controller runtime is usually treated as an implementation detail in the MTD literature. We show that it materially affects performance. We port the same Continuity-Preserving Address Mutation (CPAM) logic to three widely used controllers: Ryu (single-threaded cooperative Python), OpenDaylight, and ONOS (both multi-threaded JVM), and evaluate them on an identical 500-host campus fabric using an RFC 8456-aligned methodology with ten runs per controller. All three provide near-zero loss, sub-millisecond jitter, and preserve established sessions, but their control-plane behavior differs sharply. OpenDaylight and ONOS keep reactive latency low and stable, whereas Ryu serializes reactive flow installation behind periodic rotation work, increasing reactive RTT by about 100x and causing a small number of setup-time failures. Ryu uses far less memory, while ONOS achieves OpenDaylight-class reactive latency with the lowest CPU utilization of the three and a smaller live heap than OpenDaylight. These results expose distinct resource-versus-responsiveness operating points and show that controller selection should be treated as a first-class design decision in SDN-based MTD.
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Networking

Hybrid QKD-PQC Network Emulation through Automated and Scalable Cloud-Native Orchestration

Iván Melijosa, Javier Pérez, Borja Nogales et al. (+2 more)

The ongoing transition toward quantum-safe networking has motivated the development of hybrid network architectures integrating Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC). However, the experimental evaluation of hybrid QKD-PQC network architectures remains constrained by the high cost and limited accessibility of quantum hardware, as well as by the limited support for hybrid QKD-PQC networks in existing emulation platforms. Quditto is an open-source emulation platform originally designed for QKD networks that enables cost-effective and reproducible experimentation without requiring dedicated physical quantum infrastructure. Building on this foundation, this work presents Quditto as a hybrid QKD-PQC network emulation platform featuring automated and scalable cloud-native orchestration. The proposed platform introduces four principal contributions: a cloud-native orchestrator enabling fully automated infrastructure deployment across cloud and multi-cluster environments; an optimized provisioning workflow enabling large-scale quantum-safe network emulation; native integration of post-quantum nodes enabling unified emulation of hybrid QKD-PQC networks; and a secure key management module providing persistent and access-controlled storage of cryptographic material. Experimental validation demonstrates sublinear orchestration-time scaling with network size and successful end-to-end hybrid QKD-PQC key establishment on a representative spine-leaf deployment, thereby enabling the systematic evaluation of quantum-safe networking mechanisms in large-scale heterogeneous network environments.
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All authors Iván Melijosa, Javier Pérez, Borja Nogales, Iván Vidal, Francisco Valera