Networking and Internet Architecture Papers

ALT Integrated sensing and communications (ISAC) systems using 5G and beyond signals of opportunity enable joint wireless sensing and communication using shared infrastructure and spectrum resources. While multistatic ISAC can increase sensing coverage with spatially distributed receivers, dense and dynamic clutter can significantly degrade bistatic range and Doppler estimation, leading to errors in target localization and velocity estimation. This paper presents a multistatic ISAC framework with clutter suppression for high-fidelity estimation of target position and velocity. Distributed clutter is modeled with randomly located scatterers with varying Doppler spreads and radar cross sections to capture dense and dynamic clutter effects. A broad range of conventional signal processing baselines are evaluated for clutter suppression, showing limited robustness under strong clutter conditions. To address this limitation, deep learning-based clutter suppression methods are introduced to impro

ALT Research project 6G NeXt is considering true 3D holography as a use case, setting requirements on the communication as well as the computing infrastructure. In a future holographic communication service, clients are widely spread in the network and cooperatively interact with each other. Especially for holographic communication high processing power is required as well. This makes a high-speed distributed backbone computing infrastructure, which realizes the concept of split computing, inevitable. Furthermore, tight integration between processing facilities and wireless networks is required in order to provide an immersive user experience. This paper illustrates true 3D holographic communication and its requirements. Afterward, an appropriate solution approach is elaborated. Here, novel technological approaches are discussed based on a proposed overall communication and computing architecture.

ALT Federated learning traditionally relies on a fixed aggregation rule despite shifting client state and wireless dynamics. We present FedMaJ, a framework that dynamically selects aggregation rules via Majority Judgment voting. During voting rounds, clients evaluate candidate models across performance, resource utilization, channel condition, and fairness metrics. The server adopts the majority-preferred rule and retains it until the next vote, scheduled dynamically based on accuracy trends. Evaluated in over-the-air federated learning with explicit energy and resource cost modeling, synthetic and Fashion-MNIST experiments show that FedMaJ achieves competitive learning performance and superior aggregation-influence fairness. Under extreme-evaluation perturbations, Majority Judgment yields significantly more stable rule selections than mean-based scoring, establishing ordinal voting as a robust control layer for adaptive aggregation.

ALT Emerging intelligent wireless networks increasingly rely on autonomous AI agents to adapt spectrum usage to dynamic and heterogeneous operating conditions. However, collective learning is challenging when agents experience different local interference environments and cannot exchange raw observations due to communication-overhead and privacy constraints. We propose Federated Agentic Spectrum Coordination with Interference Awareness (FASCIA), a federated multi-agent deep reinforcement learning framework for adaptive spectrum coordination. In FASCIA, each network manager acts as an autonomous AI agent that perceives its local interference environment, accumulates experience in a persistent local memory, and learns an adaptive spectrum coordination policy through local interactions. Coordination among agents is achieved through periodic federated aggregation of locally trained models, without exchanging raw sensing data or experience. Since spectrum decisions made by one agent alter the e

ALT Network-wide flooding in mobile multihop networks selects relays from locally maintained topology information. Mobility makes this information outdated, leaving two basic responses: add redundant relays or refresh the topology and re-select relays more frequently. How these mechanisms trade reliability for overhead, however, is not well quantified. We quantify this trade-off on a public trace of 157 vehicles using Optimized Link State Routing version 2 (OLSRv2) and exact minimum-cost relay sets. Twofold coverage increases median relay-cover lifetime by only 2.3x, rather than about an order of magnitude as predicted by an independent-hazard model. The limitation is structural: 43.6 % of two-hop neighbours have only one possible relay, and newly appearing neighbours invalidate existing covers. Moreover, the delivery gain of a second relay falls from 0.061 to 0.016 when relay selection uses instantaneous link state, showing that redundancy mainly compensates for outdated topology informat

ALT Distributed quantum computing (DQC) connects multiple quantum processing units to overcome the scale limits of monolithic hardware. We introduce SeQUeNCe.DQC, a new top-level module within the SeQUeNCe package that compiles monolithic circuits into topology-aware execution plans and coordinates their execution using SeQUeNCe's existing quantum-network modules and protocols. We validate SeQUeNCe.DQC by reproducing a two-node trapped-ion Grover experiment in simulation and comparing success probabilities with the reported 71% benchmark. We then evaluate how compiler strategy, topology, and memory capacity jointly govern distributed circuit execution time and success rate. In these experiments, our hybrid compilers successfully identified mixed TeleGate/TeleData execution plans that outperformed pure gate-teleportation strategies. By capturing the physical overhead of distributed quantum primitives, SeQUeNCe.DQC provides a rigorous platform for co-designing DQC software and hardware archi

ALT Effective coral reef protection and restoration demands continuous, long-term underwater monitoring. However, current solutions largely rely on fragmented, manual labor and costly infrastructure, or are severely limited by constrained battery lifespans. In this paper, we present CoraLive, an ultra-low-power underwater camera system for long-term, continuous coral reef monitoring. CoraLive presents a decoupled underwater-shore co-design that minimizes on-board computation and storage, offloading heavy reconstruction and segmentation tasks to offline shore-side processing. To achieve this, CoraLive introduces a dual-rate sensing scheme combining frequent low-resolution frames with sparse high-fidelity samples and a content-adaptive update mechanism that extracts and stores only compact, task-specific data, eliminating the need to save raw video frames. We implement CoraLive on a custom STM32U575-based dual-camera prototype with device-level computation and storage optimization. Compared

ALT Wireless digital twins enable network policies to be evaluated before deployment, but reproducing channel effects within a running protocol stack requires continuous waveform processing under strict timing constraints. We present MIRAGE, a GPU-accelerated channel emulator that applies propagation effects directly to in-phase and quadrature samples exchanged by software 5G New Radio stacks. MIRAGE combines batched multiuser, multiantenna filtering with timestamped shared-memory transport and radio-driver integrations for OpenAirInterface and OCUDU. External propagation models supply channel responses asynchronously, while versioned buffers ensure consistent coefficients within each processing interval. Experiments on one NVIDIA H100 GPU evaluate waveform accuracy, processing time, channel updates, and full-stack traffic delivery. At 61.44 million samples per second, eight single-antenna links, four two-by-two links, and one four-by-four link meet the tested 0.5 ms bidirectional processi

ALT We study bistatic UAV detection using dual use amplify and forward (AF) relays in satellite aerial ground integrated networks. The target reflects the pilot, payload, and relay noise, allowing the exact finite sample Neyman Pearson detection probability to be derived in noncentral chisquare form under Gaussian payload signaling. The AF cascade introduces a variance signature even without payload transmission and leads to a rate-constrained pilot allocation, distinct relay-power limits, and phase-dependent variance changes under residual direct-path interference. For a decorrelated or suppressed direct path, we further derive a phase-only generalized likelihood ratio test with exact single-integral error probabilities at an assumed target magnitude, the correlated case retains a known-coefficient detection law. A 15-GHz low Earth orbit link budget and Monte Carlo simulations validate the analysis.

ALT The Scalable Matching Model (SMM) is a useful abstraction for reconfigurable datacenter networks. In SMM, a set of n (Top-of-Rack) electrical packet switches is connected via a set of optical circuit switches (OCS), each realizing a p × p matching, where p is the number of ports per switch. In the case when p ≪ n, bounded-degree networks utilize only a small portion of the OCS infrastructure at each point in time, especially when traffic patterns are skewed and bursty. In this work, we propose the Opportunistic Optical Link Scheduling (OOLS), a set of lightweight OCS link scheduling routines, designed to augment bounded-degree reconfigurable network topologies by leveraging idle OCS resources. We evaluate the performance of OOLS in the SMM through simulations, using synthetic and real-world data-center workloads. Our simulation results show that OOLS substantially improves performance, lowering communication costs and boosting throughput.

ALT This dissertation investigates whether Natural Language Processing (NLP) based semantic message prioritisation can improve the treatment of urgent disaster messages in opportunistic emergency communication, using the 2015 Nepal earthquake as a case study. TF IDF with Logistic Regression and BERT are used to classify social media messages into nine humanitarian categories, which are mapped to four routing priorities from P1 to P4. These priorities are integrated into the ONE simulator within a controlled Kathmandu earthquake scenario using a modified Binary Spray and Wait router. BERT achieves higher priority accuracy and P1 recall than Logistic Regression, while reducing under prioritisation and severe priority errors. However, improved NLP performance does not produce uniform network gains. P1 delivery probability remains high and similar across routing conditions, and no consistent latency reduction is observed. The clearest difference appears under buffer pressure, where the BERT co

ALT Radio maps provide essential wireless propagation knowledge for network optimization and autonomous platforms. Acquiring dense measurements, however, is prohibitively costly, requiring active sampling to prioritize informative measurements. Existing active sampling methods typically rely on variances from prescribed spatial models or learned pointwise uncertainty, which may not fully capture the spatial correlation of different measurements. This paper proposes GraphBARC, a graph-structured active radio map construction framework that integrates learned spatial modeling with analytical Bayesian inference. A graph network learns correlated node and edge factors using only acquired measurements, inducing a joint full-map belief that supports analytical observation updates without retraining. Rather than immediate one-step acquisition, it adopts a nonmyopic sensing strategy that evaluates joint map-wide Bayes risk reduction over feasible multi-step routes, accounting for information redun

ALT Integrated sensing and communication allows wireless networks to recognize human activity from the channel estimates that the receiver computes anyway. The inference, however, runs on the general-purpose processors of the base station, access point or handset, which are the first components a device switches off to save energy. Always-on sensing therefore requires a classifier that is small enough to remain powered while the rest of the system sleeps. We investigate whether Spiking Neural Networks (SNNs) can provide such a classifier. To this end, we generate SNN accelerators for micro-Doppler activity recognition on the public DISC dataset (IEEE 802.11ay at 60 GHz) with the open-source Spiker+ framework, and implement them together with a spike encoder on a ZCU208 RFSoC, so that the complete chain from micro-Doppler frame to activity label runs in programmable logic. A fully connected SNN with 132 neurons achieves 94.7 % accuracy (90.7 % macro-F1) on unseen subjects and performs an in

ALT Mobile Internet-of-Things (IoT) endpoints such as connected vehicles, wearables, and drones continuously cross heterogeneous network domains, creating handover-driven attack surfaces that static, cloud-anchored detectors cannot follow. However, three obstacles block real-time detection on such roaming endpoints: detection state is lost at every inter-domain handover; transmitting raw telemetry to a fixed detector exhausts the uplink and the device battery; and full-depth language model inference cannot meet millisecond latency budgets on constrained hardware. To tackle these challenges, we propose MAPLE-Sec, a mobility-aware partitioning framework that splits a compact, security-tuned language model across a roaming device and its nearest edge node. First, a trajectory-conditioned partition scheduler recomputes the split point from predicted handover timing, residual battery, and uplink quality. Second, a token-budgeted early-exit mechanism emits a verdict at the shallowest layer that

ALT Flow-based traffic classifiers trained on Ethernet data may lose accuracy when applied to Wi-Fi traffic. This paper proposes a five-stage procedure for selecting and constructing features that enable Wi-Fi flow classification using models trained only on Ethernet data. The procedure removes the transport protocol identifier, examines rate dependence, compares feature distributions and the information each feature provides for the application classes, reduces redundancy, and constructs normalized packet-size and temporal features. Ethernet and Wi-Fi traffic are simulated in ns-3, with Wi-Fi using Best Effort access category. Labelled Wi-Fi data from six development scenarios are used for feature design, while a separate scenario is reserved for final testing. The evaluation compares feature sets, examines threshold sensitivity, and analyses classification errors under different traffic conditions.

ALT Wake-up radios (WURs) reduce energy consumption by continuously monitoring the channel for a wake-up signal (WUS) while the main receiver remains asleep. Neuromorphic computing offers a promising approach to ultra-low-power WUS detection through sparse, event-driven processing. However, realizing these benefits requires spike-like inputs, which conventional orthogonal frequency-division multiplexing (OFDM) waveforms do not naturally provide. To enable neuromorphic WUS detection in OFDM-based systems, we introduce a neuromorphic wake-up signal (nWUS) that can be generated by a standard OFDM transmitter. The proposed design superposes frequency-domain vectors with linear phase progressions across the OFDM subcarriers, producing a time-domain waveform with sparse amplitude peaks at predefined positions. These peaks encode the wake-up signature in a spike-like representation that facilitates neuromorphic processing at the receiver. Building on this structure, we then propose a noncoherent

ALT Coordinated spatial reuse (Co-SR) in IEEE 802.11bn increases area throughput through simultaneous transmissions, yet the combinatorial configuration space makes real-time optimization challenging. We propose a sample-and-score approach: draw random Co-SR configurations, score each with a surrogate model, and select the best. The surrogate is trained offline and deployed without online fine-tuning or sequential exploration, yet performs close to multi-armed bandit and heuristic baselines that need multiple interaction steps or extensive measurements. On unseen topologies with 2-16 access points, it recovers 52-77% of a perfect surrogate's throughput. A perfect sample-and-score approach reaches 95% of the upper bound throughput with 8192 candidates.

ALT Human errors remain the leading cause of network outages. To prevent them researchers propose general verifiers that analyze the network behavior. Yet, their (symbolic) models struggle to accurately reflect network devices, they fail to scale to large networks, and report misbehavior, not what configuration line caused it. We attribute all three challenges to specifications that describe the intended behavior but not how operators intend to achieve it—the network's design: which protocols to run and how to combine them. We present Axiom, a language for expressing network designs, and a checker that finds every violation and the responsible commands. Operators state their design decision as rules over configuration commands. Axiom then compiles them into a single Datalog program that checks the entire network directly on its configurations, not on the state they produce. Our prototype checks a 754-router network within seconds. Ultimately, a design enforced on every keystroke could give

ALT Vehicle-to-vehicle (V2V) communication can improve road safety and traffic efficiency, yet its deployment remains limited by the need for dedicated communication hardware and supporting infrastructure. Meanwhile, production vehicles are already widely equipped with millimeter-wave radars for sensing. To unlock their communication potential, this paper presents RadarCom, a software-defined system that enables direct radar-to-radar communication on unmodified commercial off-the-shelf (COTS) automotive mmWave radars while preserving their native sensing functionality. The key insight is that mutual inter-radar interference, traditionally viewed as harmful, can be repurposed as a communication carrier. Based on this insight, RadarCom introduces slope-mismatch-based discovery, adaptive chirp synchronization, differential coding on range-FFT, and sensing-aware interference suppression to enable simultaneous communication and sensing without hardware modification, waveform redesign, or infras

ALT Network slicing is a key enabler for the coexistence of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) in 5G networks. However, allocating limited radio resources to heterogeneous slices while satisfying stringent quality-of-service (QoS) requirements remains challenging, particularly in the ultra-reliable regime where conventional effective-capacity-based approaches may lead to excessive resource allocation. To address this issue, this paper proposes a martingale-based resource block (RB) allocation framework for coexisting URLLC and eMBB slices. By constructing exponential martingale processes using the moment generating functions (MGFs) of the arrival and service processes and applying Doob's maximal inequality, explicit non-asymptotic upper bounds are derived for the URLLC delay violation probability and the eMBB backlog violation probability. Based on the resulting theoretical stability conditions, a low-complexity bisection algorithm is dev