Networking and Internet Architecture Papers

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

ALT Radio-frequency (RF) digital twins (DTs) enable site-specific wireless propagation prediction by explicitly reconstructing the physical environment and modeling electromagnetic interactions. Although physics-based DTs reduce reliance on exhaustive measurements, systematic bias relative to real radio data arises from environmental uncertainty, material inaccuracies, and geometric abstractions. Calibration using field measurements is therefore necessary but costly under limited sampling budgets. This paper studies active RF DT calibration, in which measurement locations are selected sequentially and in an online manner to estimate a calibration offset under strict measurement budget constraints. A physics-based RF DT pipeline is developed, integrating semantic geospatial data from OpenStreetMap, 3D modeling in Blender, and deterministic ray tracing in Sionna to capture dominant building and foliage effects. This RF DT is calibrated using path-loss measurements collected with software-def

ALT Reliable low-latency connectivity for real-time applications is a key requirement for 5G and future 6G non-terrestrial networks. Low Earth Orbit satellites enable global coverage but impose long propagation delays, rapid channel variations, and delayed feedback, which challenge conventional link adaptation algorithms designed for terrestrial networks. To address this problem, the paper proposes a Multi-Level Outer-Loop Link Adaptation (ML-OLLA) algorithm that assigns a dedicated target block error rate and an independent adaptation process for each transmission attempt. With ML-OLLA, early transmission attempts use higher-rate modulation and coding schemes (MCSs), while later ones use more robust MCSs. The core contribution of this work is a low-complexity data-driven parameter selection method that determines ML-OLLA parameters. Numerical results obtained with ns-3 show that the proposed ML-OLLA algorithm increases the system capacity by up to four times and reduces the minimum suppor