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Computer Science Networking

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Monday 14 September

Networking

Burst-mode timing recovery based on fourth-power phase detector for passive optical networks

Ji Zhou, Haide Wang, Xiaofeng Zhang et al. (+5 more)

Driven by the ever-increasing capacity demands, 50G passive optical network (50G-PON) is ready for practical application. It is highly challenging to realize 50GHz burst-mode analog components; therefore, based on 25GHz burst-mode analog devices, burst-mode digital signal processing (DSP) is introduced to achieve the reception and processing of 50Gb/s on-off keying burst signals. To optimize the power consumption and area of the DSP chip, a one-sample-per-symbol (1-SPS) analog-to-digital converter has been applied in 50G-PON. One of the main challenges is implementing burst-mode timing recovery (BM-TR) for the 1-SPS burst signal in 50G-PON. In this paper, we first propose a BM-TR based on the fourth-power phase detector (4PPD) for 50G-PON. We mathematically verify that 4PPD can directly compute the timing phase offset (TPO) from the 1-SPS signal without using training sequences, allowing for immediate BM-TR initialization within 20 cycles to prevent long convergence times. After the initialization, the feedback loop structure tracks the TPO changes based on the sign of 4PPD, the loop filter, and the numerically controlled oscillator. In conclusion, training-sequence-free 4PPD-based BM-TR achieves low burst overhead via fast convergence and is particularly effective for handling burst signals in 50G-PON.
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All authors Ji Zhou, Haide Wang, Xiaofeng Zhang, Zhiyang Liu, Miao Yu, Changyuan Yu, Liangchuan Li, Xiangjun Xin

Sunday 13 September

Networking

ABDS: Classifying Protocol Divergence Causes via AS-Boundary Correlation

Biplab Kumar Das

Network operators frequently encounter protocol divergence—where ICMP, TCP, and UDP experience different treatment along a path. Existing tools identify where divergence occurs but not why, forcing manual investigation that delays resolution. We present two contributions: (1) the AS-Boundary Divergence Score (ABDS), a metric that correlates protocol divergence locations with BGP topology to automatically classify causes as routing-policy-driven versus internal-policy-driven; and (2) the localhost response signature, an empirical finding that security appliances return 127.0.0.1 in TTL-exceeded messages to mask their IPs. We evaluate ABDS against 496 targets across 8 categories (Finance, Government, Education, Healthcare, Tech, E-commerce, Media, International), with multi-vantage validation from 10+ global locations confirming routing consistency. ABDS achieves 100% classification accuracy on verified divergent targets (50/50), significantly outperforming majority-class baseline (54%, McNemar's p < 0.001). The localhost signature achieves 100% precision (18/18, 95% CI: [82%, 100%]) in identifying security appliances across 9 categories including government (IRS, USPS, VA), finance, and healthcare. Our open-source Rust implementation enables automated escalation decisions, reducing mean-time-to-resolution for network operators.
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Networking

Rethinking Software-Defined Networking Link Discovery with Dynamic Randomization

Mingming Chen, Teryl Taylor, Frederico Araujo et al. (+3 more)

Software-defined networking (SDN) separates the control and data planes, enabling programmable, centralized network management. A core SDN service is topology discovery — a periodic process that identifies network links. However, our analysis of five open-source SDN controllers and ten discovery protocols shows that all remain vulnerable to at least one form of link-fabrication attack. The common root cause is their reliance on traditional Link Layer Discovery Protocol (LLDP) packets, whose static identifiers expose their discovery purpose and attract adversaries to exploit them. We propose ChameleonDisc, a dynamic link-discovery protocol that simultaneously prevents and detects topology-poisoning attacks by eliminating this root cause. Our key insight is that an SDN controller can infer topology without embedding meaningful information in discovery packets. ChameleonDisc removes targetable static LLDP signatures and employs a moving-target defense that combines decoy, obfuscation, and camouflage techniques instantiated dynamically at runtime to prevent topology poisoning and detect manipulation. We implement ChameleonDisc on OpenDaylight and demonstrate effectiveness against all identifier-based topology-poisoning attacks. On a 252-link topology, median convergence is 1.37–4.53\,s for legitimate link changes and 4.06\,s for malicious relay detection, with 13.0 percentage points of additional mean CPU and negligible retained-heap difference. Across topologies up to 816 links, ChameleonDisc provides a tunable security–performance trade-off; expanding discovery intervals reduces CPU and mapping state at the cost of increased attack-detection latency.
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All authors Mingming Chen, Teryl Taylor, Frederico Araujo, Benjamin E. Ujcich, Thomas La Porta, Trent Jaeger

Networking

Self-Adaptive Threshold ALOHA

Ahsen Topbas, Aimin Li and Elif Tugce Ceran

We propose Self-Adaptive Threshold ALOHA (SATA), a distributed random-access policy to minimize the Age of Information (AoI). SATA uses 1-bit broadcast feedback and requires no explicit coordination or message passing among nodes. {We show that this minimal feedback is sufficient for each node to perfectly track the number of active users and adjust its access probabilities accordingly.} For any initial network state, SATA converges to a collision-free TDMA steady state in finite time whenever the age threshold satisfies $\Gamma \geq n$, where $n$ is the network size. We derive closed-form expressions for the long-term average AoI and throughput in steady state, and establish that, for $\Gamma=n$, the expected transient duration scales as $O(n\log n)$. Simulation results confirm that SATA closely approaches TDMA performance across a wide range of network sizes, significantly outperforming Slotted ALOHA, Threshold ALOHA, and 1-persistent Threshold Slotted ALOHA. Notably, the performance gap between SATA and the benchmark random-access policies becomes more pronounced as network size grows.
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Networking · Cross-listed from eess.SP

HELENA for 5G NR LEO NTN Channel Estimation: A Comparative Evaluation

Miguel Camelo Botero, Nina Slamnik-Kriještorac and Johann Marquez-Barja

Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and synchronization impairments that may require NTN-specific architectures. We test whether High-Efficiency Learning-based channel Estimation using dual Neural Attention (HELENA), originally designed for terrestrial channels, remains effective after NTN retraining and suitable across high-performance and power-constrained inference platforms. Its unchanged architecture is trained on paired receiver-compensated (NTN-1) and residual-impaired (NTN-2) datasets and compared with eight terrestrial-origin models trained on the same NTN data and the NTN-specific MDELAN-SISO. HELENA achieves the lowest observed SNR-averaged NMSE among the DL estimators in both conditions, including 55.8-62.7% lower linear-scale NMSE than MDELAN-SISO. All DL models degrade in NTN-2, demonstrating the challenge posed by residual Doppler and its associated impairments. On an RTX PRO 4500, HELENA achieves 0.0595 ms 99th-percentile (P99) inference latency, 88.1% below the 0.5 ms budget, with lower energy than its closest attention-based competitors. On a 10 W Jetson Orin NX, it retains a favorable accuracy-energy trade-off, but no model meets the P99 budget. Thus, HELENA needs no NTN-specific redesign for the evaluated task, while embedded tail latency remains an open challenge.
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Networking

Energy-Aware LoRaWAN Design for Long-Lived Agricultural Sensing: Insights from a Multi-Year Deployment and Controlled Platform Comparison

Carl Dickinson, Shishir Nagaraja and Chuadhry Mujeeb Ahmed

Agricultural IoT nodes are often far from mains power and have limited access for maintenance. Therefore, IoT deployments benefit from low-power, long-range communication such as LoRaWAN. However, communication alone does not guarantee multi-year operation. Previous work has measured communication energy, modelled system lifetime, and documented long-lived deployments. This paper connects long-term deployment evidence with controlled platform measurements. The AgriTrust deployment has operated for 2 years and 8 months using MKR WAN1310-based Squirrel Box nodes. Building on this evidence, we compared the MKR WAN1310 and STM32WL LoRaWAN platforms using time-resolved current traces during confirmed-uplink cycles. For this outdoor comparison, we tested nine combinations of payload size and data rate with three repetitions each. Both platforms achieved 3/3 ACKed packets in every condition. The STM32WL had lower cycle energy than the WAN1310 in all tested conditions. Cycle energy ranged from 0.0350 J to 0.3550 J for STM32WL and from 0.0698 J to 0.5662 J for WAN1310. These communication events are brief, while sleep and leakage currents persist between cycles. The STM32WL sleep current was recorded at 59.6 nA, while the WAN1310 sleep current was 104 microamperes, approximately 1745 times that of the STM32WL. This difference shows that sleep current is a first-order determinant of long-term viability. However, the whole-system energy budget also depends on sensing loads, duty cycle, communication energy, and harvested-energy support. The study should be treated as a controlled characterisation, not a universal comparison across all sites, firmware stacks, or LoRaWAN modes.
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2609.14732 · 13 Sep · 8 pages, 5 figures. Presented at the 4th International Workshop on Long and Short Range Wireless Technologies Applied to IoT for Networks of Tomorrow (LS-NoT 2026), co-located with DCOSS-IoT 2026, Reykjavik, Iceland, 22-24 June 2026

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Networking

Toward a Layer-2 Trigger for AI/ML Lifecycle Management in 6G

Dharmendra Kumar

3GPP has progressively expanded AI/ML lifecycle management in the radio access network, from one-sided model control to Release 20 support for two-sided CSI-feedback model pairing. Yet a basic control question remains: when monitoring detects degradation, how quickly must a corrective action take effect? To expose this dependency, we stress-test three activation and rollback strategies in a surrogate regime-shift environment using 150 independently trained PPO candidate policies, each evaluated over 20 matched noise realizations. We add 0-40 control-step delay only to corrective lifecycle commands. With no added delay, stability-gated blending reduces mean post-shift cumulative SLA deficit from 47.02 to 7.57 violation-steps relative to hard cutover; at 40 steps, the deficit rises to 44.38, only 5.6% below the hard-cutover baseline. KPI-threshold rollback loses its advantage within only a few control intervals, while blending degrades more gradually. These results do not set a physical 6G latency bound. They show why timing requirements matter for corrective actions: lifecycle performance depends on when the command takes effect. Motivated by Layer-1/Layer-2 Triggered Mobility, we examine a standards split in which Layer 3 retains lifecycle configuration while a compact Layer 2 trigger is considered only for the latency-critical subset, together with pair-consistency, local-fallback, and security/freshness requirements.
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Networking · Cross-listed from cs.IT

Channel-Aware Selection of Folded Bloom Filters for Distributed Systems

John Cartmell, Mihaela Cardei and Ionut Cardei

Periodic Bloom-filter transmission can impose substantial overhead in communication-constrained distributed systems. Lossless compression preserves membership behavior but provides a single transmission size, whereas established OR folding produces smaller representations with higher false-positive rates (FPRs) while preserving the no-false-negative property. This paper investigates channel-aware selection among OR-folded representations. The sender retains an unchanged canonical filter, constructs a catalog satisfying a maximum FPR, and selects the FPR-qualified representation with the largest retained length supported by the communication resources available at each reporting opportunity. Unlike folding driven principally by cardinality and false-positive constraints, selection is driven by time-varying communication conditions. Using two phishing URL datasets, the framework is evaluated under Five-State Markov Capacity, Gilbert–Elliott burst-error, and Rayleigh block-fading models. Channel-aware folding improves communication efficiency and receiver freshness relative to complete-filter and lossless-compression baselines when communication opportunities vary substantially. Under the more favorable Gilbert–Elliott model, it remains competitive in efficiency while maintaining the freshest receiver state. These results show that FPR-qualified folded views provide useful transmission operating points when a recent lower-fidelity update is preferable to delaying a larger representation.
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Networking

Breaking the Duplex Barrier: Lane-Granularity OCS Scheduling for LLM Training

Bangbo Liang, Yupeng Chen, Sicheng Zhao et al. (+6 more)

Optical circuit switch (OCS) can reconfigure physical connectivity to match the predictable communication schedules of large language model (LLM) training. Although each OCS light path is physically simplex, existing demand-aware OCS schedulers allocate capacity in duplex-port pairs, forcing equal bandwidth in both directions and stranding capacity under asymmetric node-pair traffic. This paper present LACE, the first offline OCS schedule compiler that independently allocates transmit (TX) and receive (RX) lanes for LLM training. Without changing the selected collective algorithms, operation order, or rank placement, LACE reconstructs directed node-level demand, jointly determines which consecutive operations share a configuration and how many simplex circuits serve each direction, and realizes these allocations as physical lane bindings and optical paths under per-node lane-inventory and multi-OCS fabric constraints. Software acknowledgments carry feedback over independently provisioned return paths, while coordinated link configuration and recovery verify each configuration before communication resumes. On a separate three-server testbed using fixed topologies and matched per-port rate limits, LACE's asymmetric connectivity achieves $1.80\times$ speedup for communication replay and $1.27\times$ for GPT-2 training over a symmetric-topology baseline. At larger scale, simulations of LLaMA-3.1 70B and 405B schedules with sixteen 400-Gb/s ports per server show that LACE achieves $1.21$–$2.04\times$ communication speedup over the latest duplex OCS scheduler.
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All authors Bangbo Liang, Yupeng Chen, Sicheng Zhao, Peihao Huang, Di Yang, Bohua Xu, Bin Yang, Shizhen Zhao, Guo Chen

Networking · Cross-listed from cs.CR

Enc53: DNSSEC-Anchored Stateless Tickets for Post-Quantum Authoritative DNS

Minh Hoang Tran, Munshi Rejwan Ala Muid and Taejoong Chung

DNSSEC authenticates RRsets, but does not provide endpoint authentication or channel security. DNS-over-TLS (DoT) and DNS-over-QUIC (DoQ) can facilitate such needs, but were designed for the stub-to-resolver hop, where stable long-lived connections amortize the expensive initial setup. The recursive-to-authoritative path's high fan-in and nonuniform per-resolver query frequency invert said dynamics. Post- quantum primitives further sharpen this mismatch: an ML-DSA WebPKI certificate chain crosses TCP's initial window, a cold PQ DoQ may incur up to about 140 times the total bytes of the same query over UDP. A survey of TLD and 2LD nameservers further bounds connection lifetimes, with almost half surveyed imposing limits on even non-idle connections. We present Enc53 – a stateless session ticket protocol enabling efficient authenticated authoritative DNS encryption. Enc53 splits DNS encryption into 2 phases: a short-lived, DNSSEC-anchored, TLS- authenticated provisioning on the initial query in the 1st, and a steady state of 1-RTT AEAD-encrypted UDP DNS queries in the 2nd. Enc53 is server-side stateless: recursive resolvers hold the traffic secret and session ticket, authoritative nameservers hold only a symmetric STEK. We implemented Enc53 in Knot DNS. After provisioning, a steady state Enc53 exchange costs about 570 B – roughly 3 times a plain UDP query – and lands within 1 ms of the unencrypted UDP baseline. Resumed PQ-ADoT pays 7.7 times the bytes and 3 times the latency; resumed PQ-ADoQ pays 10 times the bytes for the same latency. When evaluated against a root server query trace, Enc53 achieves 2-fold compute efficiency over ADoT/ADoQ, 3-fold memory efficiency over ADoT, and 12-fold memory efficiency over ADoQ. Finally, when deployed in conjunction with FN-DSA-512 PQ-DNSSEC, the joint Enc53-DNSSEC UDP datagram remains below the 1232B buffer limit.
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Saturday 12 September

Networking

Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies

Muhammad Zain Ul Abideen, Prathapasinghe Dharmawansa, Nurul Huda Mahmood et al. (+1 more)

Software-defined vehicular networks (SDVNs) under high mobility and fluctuating traffic demand offer programmable, centralized control for latency-sensitive intelligent transportation systems. However, data-plane Quality of Service (QoS) is often degraded by control-plane overload due to frequent handovers and dense vehicle-to-infrastructure (V2I) contacts. To address this, we propose two lightweight mechanisms for low-latency control-plane resizing in multi-controller SDVNs. The first - Control-plane Centric Control-plane Resizing Mechanism - proactively offloads roadside units from overloaded controllers to underloaded or idle ones when a predefined load threshold is exceeded, preventing prolonged overload with minimal decision latency. The second - Data-plane Centric Control-plane Resizing Mechanism - triggers resizing based on observable data-plane QoS degradation, such as average round-trip time exceeding a QoS threshold, aligning control-plane adaptation with V2I service experience. Both mechanisms are implemented and evaluated on Mininet-WiFi emulation testbeds with realistic worst-case vehicles mobility. Compared to fixed single-controller and static multi-controller benchmarks, the proposed algorithms significantly reduce end-to-end delay and packet loss while improving load balancing rate.
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All authors Muhammad Zain Ul Abideen, Prathapasinghe Dharmawansa, Nurul Huda Mahmood, Chafika Benzaïd

Networking · Cross-listed from cs.IT

Beam Squint and Aperture–Bandwidth Limitations in Wideband RIS-Assisted THz Links

Waqas Khalid, Chiew Foong Kwong, David Chieng et al. (+1 more)

This paper analyzes the ergodic rate of a wideband reconfigurable intelligent surface (RIS)-assisted terahertz link under beam squint and finite-resolution phase control. Independent Rayleigh fading on the two RIS-assisted hops produces double-Rayleigh cascaded amplitudes. Exact second- and fourth-order channel moments are derived by incorporating the frequency-dependent array response and phase-quantization errors. Moment matching then provides a Gamma approximation for the received power on each subcarrier and a tractable closed-form approximation for the wideband ergodic rate. A first-null bandwidth approximation characterizes the aperture–bandwidth limitation of frequency-flat RIS control. Monte Carlo simulations validate the analytical framework.
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All authors Waqas Khalid, Chiew Foong Kwong, David Chieng, Qianyu Liu

Networking

Towards a Connected Heterogeneous All-Medium Integrated Network (CHAIN) for Converged Connectivity Across Land, Sea, Air, and Space

Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill et al. (+12 more)

Next-generation connectivity depends on data traversing multiple physical media within a single end-to-end path, yet research in optical fibre, free-space optical and radio wireless, non-terrestrial networks, and underwater communications has advanced largely in isolation. This fragmentation has a wider adverse impact on communication performance, deployment and adaption as the most demanding open problems in next-generation connectivity, cross-medium channel characterisation, transport-layer protocol design across heterogeneous latency regimes, and cross-domain orchestration, sit at the boundaries between domains and can only be addressed by bringing these challenges together in cross-domain systems. Space-air-ground integrated network research has begun to treat three domains analytically, and its recent extension to the sea surface represents the most ambitious multi-domain framework proposed to date, yet neither has produced experimental results, a deployed system, or a standards engagement. The subsurface and maritime domain remains absent from both. This paper proposes the connected heterogeneous all-medium integrated network (CHAIN), a framework that treats the undersea, maritime, terrestrial, aerial and space domains as a single design space. We identify an all-photonic network backbone as the unifying physical infrastructure, Artificial intelligence (AI)-driven orchestration as the cross-domain control layer, and the joints between domains as a unification challenge. We survey the state of the art across all domains and the existing cross-domain literature, develop the CHAIN framework and its technical pillars, characterise the five core open problems the field must resolve, and set out a research roadmap from near-term measurement campaigns and testbed validation through cross-medium field trials to global-scale deployment.
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All authors Paul Anthony Haigh, Sujan Rajbhandari, Kyle Bottrill, Abderrahmen Trichili, Scott Watson, Hanaa Abumarshoud, David Benton, Sinan Sinanovic, Johannes Herrnsdorf, Peter Christopher, Mohsen Khalily, Rahim Tafazolli, Harad Haas, Martin Lavery, Wasiu Popoola

Networking

HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks

Liang-Ching Tao and Pi-Chung Wang

Multi-charger scheduling in wireless rechargeable sensor networks must weigh sensor death risk, charger energy, travel cost, return-to-base feasibility and inter-charger coordination at once, and schedulers driven by local urgency alone duplicate service and leave whole regions unattended. We present HQARRF, a two-level scheduler. Below, an interpretable ARR-F score ranks candidate clusters through an attraction term for local urgency, a repulsion term against charger crowding and a force bonus from nearby critical sensors. Above, adaptive zones compress regional state into a deadline-based risk estimate, and a gated Q-learning controller decides only whether to redirect service to a high-risk, under-served zone. Over 27 parameter points HQARRF attains the highest mean survival rate at 26, improving survival by 20.7 percentage points over the mean of five baselines and 9.2 over the strongest baseline at each point. An ablation isolates the upper level: its gain tracks how often the controller fires.
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Networking

Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán et al. (+13 more)

As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements
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All authors Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán, Fernando Torres-Vega, José María Jorquera Valero, Manuel Gil Pérez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Betül Güvenç Paltun, Leyli Karaçay, Ioannis Pitsiorlas, Marios Kountouris

Networking

Meter-Level Wi-Fi RTT Localization on a Production Enterprise WLAN

Enguang Fan, Binh Minh Tran and Klara Nahrstedt

Wi-Fi Fine Time Measurement (FTM) promises indoor localization by reusing access points (APs) already deployed for connectivity, but prior evaluations mostly use APs purpose-deployed or calibrated for ranging, leaving it unclear whether a production enterprise WLAN can provide useful localization without localization-specific infrastructure. We evaluate Wi-Fi round-trip time (RTT) localization on IllinoisNet, a live campus WLAN whose APs were placed for coverage and capacity. Using five commodity Android phones at 10 static locations across a 50 m x 30 m office floor spanning LOS, NLOS, and multipath conditions, weighted nonlinear least squares achieves a median error of 1.21 m and a 90th-percentile error of 2.68 m, with no AP replacement, repositioning, or ranging calibration. Residuals vary in magnitude and sign across APs and propagation conditions, suggesting a single global correction is insufficient and motivating AP-aware software calibration rather than new localization-specific infrastructure.
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Networking · Cross-listed from cs.CR

PQLN: Post-Quantum Security for the Bitcoin Lightning Network's Off-Chain Surfaces

Ahmet Kurt, Abdul-Salem Beibitkhan, Yacoub Hanna et al. (+1 more)

A cryptographically relevant quantum computer will break the elliptic-curve cryptography behind Bitcoin and its Lightning Network, the most widely used payment channel network. Even a post-quantum consensus upgrade of Bitcoin would not cover Lightning's off-chain surfaces, so its gossip, peer transport, invoices, payment onions, and offers need separate protection. An adversary can already record Lightning's encrypted traffic today and decrypt it once such a computer exists. Lightning can therefore move to post-quantum cryptography now, without waiting for Bitcoin, and stop such harvest-now-decrypt-later attacks along with node impersonation, invoice forgery, and payment deanonymization. In this paper, we propose PQLN, a hybrid post-quantum extension of Lightning that protects all of these surfaces with the lattice-based standards ML-DSA and ML-KEM. PQLN distributes post-quantum node identities through Lightning's gossip, hybridizes the transport handshake, adds post-quantum signatures to invoices, commits post-quantum keys in offers, and makes payment onions hybrid. Since post-quantum material is much larger than its elliptic-curve counterpart, we introduce techniques that fit it into Lightning's existing message formats and size limits. We analyze the security of PQLN against a quantum adversary and implement it in rust-lightning, a major Lightning implementation. Our evaluation with real Lightning nodes shows that PQLN nodes interoperate with unmodified nodes. The added cryptographic operations take at most 0.33 milliseconds, and the main cost is communication, since gossip data grows about tenfold with ML-DSA and about fourfold with the smaller Falcon. To our knowledge, PQLN is the first post-quantum design, implementation, and evaluation for Lightning.
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All authors Ahmet Kurt, Abdul-Salem Beibitkhan, Yacoub Hanna, Abdullah Aydeger

Friday 11 September

Networking · Cross-listed from cs.DC

mKernel: Fast Multi-GPU, Multi-Node Fused Kernels

Ziming Mao, Yihan Zhang, Shawn Wei Chew et al. (+5 more)

Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granularity of kernels, on separate streams, reduces only part of this communication cost. Fused kernels often have better performance by transmitting each output tile as soon as it is produced, but existing fused kernels are largely confined to a single NVLink domain. We present mKernel, a library of multi-GPU, multi-node fused kernels that overlap computation, intra-node NVLink communication, and inter-node RDMA at tile granularity. mKernel partitions the streaming multiprocessors (SMs) of a persistent kernel into compute and communication roles, and an on-GPU controller tunes the SM partition adaptively at run time, since the best SM partition varies with the kernel and the input shape. It structures data movement hierarchically so that data traversing the inter-node network is minimized. Finally, it drives the network from the GPU through a lightweight command queue and host proxy implemented directly on RDMA verbs, which allows the same kernels to run on any network backend (e.g. InfiniBand and on AWS EFA); we observe, surprisingly, that GPUDirect Async (IBGDA) yields little additional benefit over host-assisted GPU-initiated communication. We implement five kernels spanning tensor, sequence, and expert parallelism. On two 16-GPU H200 clusters, mKernel achieves speedups of up to 1.72x on GEMM+AllReduce and $1.88\times$ on Ring Attention.
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All authors Ziming Mao, Yihan Zhang, Shawn Wei Chew, Shuang Ma, Costin Raiciu, Yang Zhou, Scott Shenker, Ion Stoica

Networking

Virtualized 5G Tesbed using OpenAirInterface: Tutorial and Benchmarking Tests

M. Dória, V. Sousa, A. Campos et al. (+9 more)

The development of 5G and its evolutionary path to 6G brings virtualization as close as possible to the antennas. Native 3GPP systems are now software running at servers boosted by accelerator cards to cope with computationally intense signal processing. Meanwhile, the Radio Frequency (RF) front-end is still proprietary hardware, sheltering specific PHY-layer procedures like passband amplification/modulation. This approach takes advantage of the software's flexibility while keeping the complex microsecond signal processing performance from modern telecommunication systems. This paper provides tutorial material on the Core Network and Radio Access Network of OpenAirInterface (OAI) 5G stack on top of Universal Software Radio Peripheral (USRP) platforms. A set of blueprints showcases OAI's ability to provide a virtualized 5G network with different transmission capabilities and the possibility to use it with commercial mobile phones. Configuration discussions and throughput benchmark analyses follow installation and deployment instructions. Our results show that 5G prototyping using OAI and USRP frontends can lead to good reproducibility and consistent throughput.
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All authors M. Dória, V. Sousa, A. Campos, N. Oliveira, P. Eduardo, P. Filho, C. Lima, J. Guilherme, D. Luna, I. Rego, M. Fernandes, A. Neto

Networking

Support-Aware Telemetry Compression for 5G Positioning via Conditional Conflict Graphs

Mohammad Reza Deylam Salehi and Hakima Chaouchi

Geographically separated transmission/reception points (TRPs) report quantized measurements to a Location Management Function (LMF), even when the application requires only a coarse location region. We formulate this task as a distributed zero-error function-computation problem, in which each TRP transmits an index sufficient for the LMF to reproduce the required service decision. Since positioning geometry induces a sparse and nonrectangular support, independently constructed per-terminal characteristic-graph colorings are not necessarily jointly decodable. We introduce a conditional conflict graph that exactly characterizes valid single-terminal updates and develop an alternating codebook construction that preserves global zero-error decodability. In a reproducible three-TRP study with range-equivalent timing measurements and $120$ native bins, all resulting codebooks satisfy an explicit decoder-conflict test. For service-cell sizes up to $100$ m, the achieved ideal rate is $5.38$–$5.44$ bits per epoch per TRP, compared with $6.91$ bits for raw reporting and $6.56$–$6.87$ bits for a globally valid interval-based baseline. A complementary measured six-base-station TDoA study shows that $83.77\%$ of the learned native support recurs on an independent trajectory. For these recurrent tuples, the codebooks preserve the service decision exactly and reduce the ideal rate by $22.8$–$35.6\%$ for $2$–$8$ m service grids. The feasible report-tuple set also provides a single-epoch geometric-consistency check under injected timing bias. Finally, we identify the NRPPa/OpenAirInterface integration points and safeguards required for experimental implementation.
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Networking

Vertical Assessment of RF-EMF Exposure in a Building Adjacent to a Multi-Operator Shared Base Station

Ricardo Q. de F. H. Silva, Marcio E. C. Rodrigues, Fred S. R. Pinheiro et al. (+3 more)

This paper extends a previously published proposed approach for estimating worst-case exposure scenarios in buildings using publicly available Base Station (BS) parameters, applying it to a critical case involving a multi-operator shared site. Radiofrequency Electromagnetic Fields (RF-EMF) exposure was assessed on every floor of a 20-story building facing the site, revealing significantly higher levels near the center of the antennas' estimated vertical zone of interest, with a peak of 31.86 V/m, 23.95 times higher than the street-level peak value. However, due to uncertainties in the input parameters, measurements across all locations within the zone of interest are required to accurately identify the floor of maximum exposure. The results demonstrate the usefulness of the proposed estimation approach and highlight the need for improved methods that consider BS antenna configurations and current technical information to ensure accurate assessments of exposure to RF-EMF.
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All authors Ricardo Q. de F. H. Silva, Marcio E. C. Rodrigues, Fred S. R. Pinheiro, Gutembergue S. da Silva, Halysson B. Mendonça, Vicente A. de Sousa Jr

Networking

Hybrid Monitoring for Early Fault Detection in Cloud-Native 5G Systems

Anton Andersson, Sai Akshara Naineni, Mats Jansborg et al. (+2 more)

This paper presents the design implementation and evaluation of NetMon a hybrid network monitoring system designed for Kubernetes-based 5G packet core deployments specifically evaluated on Ericssons Access and Mobility Management Function AMF clusters NetMon combines eBPF-based passive kernel-level traffic observation with active TCP probing and centralized correlation to detect and localize network degradation within seconds The evaluation results demonstrate that the system detects faults as subtle as 10ms of added latency or 5 packet loss correctly attributes them to the affected infrastructure component and maintains this capability under application loads up to 50 simulated UE load The total resource overhead of 34 millicores CPU and 45 MiB memory per pod suggests that the approach is promising for further validation without impacting the monitored workload The hybrid approach addresses a gap in existing monitoring tools standard health checks cannot detect partial degradation scrape-based systems introduce detection delays measured in tens of seconds and purely passive tools cannot verify idle network paths By combining these complementary techniques and centralizing the analysis the system provides the early detection and fault localization capabilities required for maintaining service quality in cloud-native 5G infrastructure.
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All authors Anton Andersson, Sai Akshara Naineni, Mats Jansborg, Yixing Zhang, Romaric Duvignau

Networking · Cross-listed from cs.CR

A Feature-Rich Embedded NIDS with eBPF/XDP: Detector and Architecture Trade-offs

Shiqi Wu, Oleksii Koshovyi, Georgios Pseiridis Pseiras et al. (+2 more)

Distributed Denial-of-Service (DDoS) attacks remain a serious threat to transport networks, with recent attack volumes exceeding 30 Tbps, and the telecommunications industry being the main target. Recent work has yet to study the impact of the hosting software architecture on network monitoring solutions, or to assess recent algorithms for improving attack detection. This paper presents a Network Intrusion Detection System (NIDS) for DDoS detection in transport networks, developed in collaboration with Ericsson. Building on a statistical baseline, we improve detection effectiveness with an Isolation Forest trained on a wider set of flow features, extracted by GoFlowMeter, our open-source Go implementation of CICFlowMeter, and we integrate eBPF/XDP so that the NIDS filters real traffic at the kernel level. We further compare three deployments, monolithic, Kafka-based, and gRPC-based microservices, on a Raspberry Pi 5 testbed replaying the CIC-DDoS2019 dataset as real network traffic. Detection quality is governed mainly by the choice of detector rather than by the transport: the Isolation Forest raises recall and F1 score (0.965 live in the monolithic variant) over the baseline by flagging low-volume attack windows that the baseline misses. The transport is not neutral, however: gRPC reaches almost the same accuracy as the monolithic variant while adding less than 2 milliseconds of transport time per window, whereas the asynchronous Kafka pipeline trails by roughly nine percentage points and adds about 27 milliseconds. These findings clarify the trade-off between detection quality and architectural overhead when deploying a NIDS on resource-constrained hardware.
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All authors Shiqi Wu, Oleksii Koshovyi, Georgios Pseiridis Pseiras, Victor Morel, Romaric Duvignau

Thursday 10 September

Networking · Cross-listed from eess.SP

Utility-Aware Adaptive Downlink DM-RS Allocation from Uplink CSI for Lightweight O-DU dApps

Seungwon Min

Adaptive demodulation reference signal (DM-RS) placement in 5G New Radio (NR) trades pilot overhead against channel-tracking robustness, making a single static density suboptimal across heterogeneous mobility and propagation regimes. This paper studies a lightweight per-UE controller intended for O-DU-side distributed application (dApp) execution. The controller consumes eleven deployable uplink-CSI statistics and selects among six standards-derived PDSCH DM-RS configurations. Rather than optimize oracle-class accuracy, we train an 11-128-64-32-6 multilayer perceptron with a utility-aware objective that preserves the throughput structure of all candidate actions. In a large-scale trajectory-disjoint evaluation covering 294 channel/speed/SNR conditions and 7,056 observations, the proposed U-SoftCE policy significantly improves throughput over a training-selected best fixed pattern by 1.30, 0.86, and 1.13 percentage points on in-distribution, CDL channel-OOD, and 300-km/h mobility-OOD splits, respectively; paired 95% bootstrap intervals exclude zero in all three cases. An independent bit-accurate Sionna NR LDPC validation over 36 held-out conditions and 50 transport blocks per candidate retains a +1.21-point gain with a 95% cluster-bootstrap interval of [0.27, 2.13]. A native C++ implementation uses 12,070 dense parameters and requires 5.59 microseconds mean inference time, while a FlexRIC-based emulated O-DU prototype demonstrates sub-10-ms closed-loop operation. These results support utility-aware objectives for compact adaptive PHY controllers while exposing the limits of exact class-accuracy optimization.
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Networking

NDT Factory: Synthesizing Verified Network Digital Twins from Semantic Models via Multi-Agent LLM

Sudipta Acharya, Petar Djukic and Burak Kantarci

Autonomous network management requires systems that can evaluate Network Service Intents (NSIs) under varying conditions without manual implementation of analysis logic, as envisioned in TM Forum Level~4 (L4) autonomy. Behavioral Network Digital Twins (NDTs) enable such evaluation, but existing NDTs rely on pre-defined analytical logic, limiting adaptability for evolving closed-loop control. This paper introduces the NDT factory, a multi-agent software system that synthesizes executable behavioral NDTs on demand from semantic models using Large Language Model (LLM). We validate the system using a Call Admission Control (CAC) case study, where deterministic what-if analysis serves as the admission decision process. The NDT factory generates a complete CAC NDT through parallel synthesis and orchestration, achieving 100% compilation and test pass rates across multiple runs. Simulation over 300 NSIs shows 99.3% decision agreement with a reference implementation, 90% admission rate, and correct attribution of all rejections, demonstrating reliable synthesis with deterministic, verifiable execution.
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Networking

Challenges and Opportunities in the Transition from 5G to 6G Networks

Yousef Tahboub, Krishna Bodige and Ryan Tipping

The rapid adoption of 5G revolutionized connectivity worldwide, enabling faster data transfer along withthe support of a variety of use cases. However, next-gen technologies demand higher performance, as the limitations of 5G become increasingly apparent. The 6G network transition is approaching but comes with unique challenges, particularly with spectrum management, security, and data transmission. The present research considers the most significant challenges that must be addressed for a smooth and successful 5G-6G transformation to occur. Based on case studies and academic literature, the research employs a comparative approach and statistical analysis to evaluate 5G limitations and assess the innovative potential of 6G. The envisioned contribution is the identification of the most significant technical, regulatory, and security vulnerabilities, along with proposed solutions. Through these alternatives, the research will help to identify 6G networks that are secure, resilient, and applicable globally for use and additional critical infrastructure support.
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Networking

On Identifying Adversarial Intent Injection in AI-Native 6G Networks

Nilesh Chakraborty, Petar Djukic and Burak Kantarci

AI-native 6G networks have brought Intent-Based Networking (IBN) to the forefront, enabling high-level goals to be translated into network configurations. However, this abstraction opens new attack surfaces, primarily adversarial intent injection, where malicious policies are disguised within benign intent flows. The detection of attack instances might become significantly more difficult if the adversaries adopt a stealthy mode of malicious intent injection. With all these in mind, we first define a fine-grained threat model that facilitates the threat of malicious intent injection in an AI-native network. Alongside, we investigate four malicious intent injection strategies$-$ stealth-mode, random distribution, increasing frequency, and decreasing frequency- and propose a dual-path detection framework: (i) a CNN using TF-IDF features for supervised malicious intent detection, and (ii) an AutoEncoder trained exclusively on benign data for one-class malicious intent detection. Our evaluation demonstrates strong detection performance, with accuracy improving to 0.97 (~9% gain) and F1-score to 0.98 (~36% gain) over the state-of-the-art baseline.
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Networking

From Open RAN to Open Spectrum: A Programmable, Intelligent Architecture for Multi-Service Spectrum Coexistence

Michele Polese, Minh Dat Nguyen, Paolo Testolina et al. (+1 more)

Considering sharing or coexistence from the perspective of spectrum alone fails to recognize that any spectrum-enabled service also requires (i) radio and processing infrastructure and (ii) a protocol stack, including waveforms and signal processing pipelines. The efficiency of spectrum coexistence frameworks such as Citizen Broadband Radio Service (CBRS) is thus limited to optimizing resource allocation across a single dimension. How to address this limitation, however, remains an open challenge, especially considering the diversity of requirements and operational modes across spectrum services (e.g., sensing, communications, navigation, or positioning). This article introduces Open Spectrum, an architecture that brings softwarization, programmability, and open interfaces to heterogeneous spectrum services, extending the open Radio Access Network (RAN) principles beyond wireless networking. We propose to combine spectrum, services, and infrastructure in a common pool. Its resources are shared and orchestrated by a Spectrum Intelligent Controller (SIC), with plug-and-play spectrum applications, i.e., Spectrum Applications (sApps), and data-driven Radio-Frequency Interference (RFI) modeling using Digital Twins (DTs). We describe the Open Spectrum architecture, shared infrastructure pool, and operational workflows for tenant onboarding and incentives, conflict resolution, and service sharing across sensing, radionavigation, radiolocation, and cellular systems. System-level simulations using the BostonTwin urban DT and Sionna ray tracing show that there exist performance-driven incentives in sharing infrastructure and sharing across multiple services, enabling increased access to spectrum and improvement in median Signal to Interference plus Noise Ratio (SINR) of up to 12 dB.
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All authors Michele Polese, Minh Dat Nguyen, Paolo Testolina, Tommaso Melodia

Networking

Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery

Tarciana C de Brito Guerra, Taline Nóbrega, Edgard Morya et al. (+2 more)

Electroencephalography (EEG) is a fundamental tool for understanding the brain's electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.
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All authors Tarciana C de Brito Guerra, Taline Nóbrega, Edgard Morya, Allan de M. Martins, Vicente A de Sousa Jr

Networking

Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis

Deyvid L. Leite, Pablo Javier Alsina, Millena M. de M. Campos et al. (+2 more)

The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.
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All authors Deyvid L. Leite, Pablo Javier Alsina, Millena M. de M. Campos, Vicente A. de Sousa Junior, Alvaro A. M. de Medeiros