ALT Space-based artificial intelligence (AI) is moving beyond isolated onboard processing toward constellation-scale computing over optical inter-satellite links (OISLs). Yet current infrastructures remain fragmented, making end-to-end (E2E) coordination across heterogeneous and multi-vendor systems difficult. This article examines the StarMind vision and develops a reference framework for distributed orbital computing. It organizes the system into five layers and defines six standardizable interfaces that link mission intent to resource conditions and trustworthy result delivery. The framework also clarifies the operational constraints that determine whether orbital cooperation is worthwhile. In a representative synthetic aperture radar/interferometric SAR (SAR/InSAR) case study, the framework reduces E2E latency to 41-min, compared with 61-min for localized onboard computing and 146-min for ground-based processing. This gain does not rely on faster processors in orbit. Instead, it comes

ALT Mobile networks are evolving from human-centered, data-oriented communication infrastructures toward machine- and/or agent-dominant, task-oriented platforms that are to serve the rapidly increasing networked machines and embodied agents. In order to fulfill the newly arose complex task objectives, the framework of future mobile network is to be innovated to reflect and facilitate the intrinsic interplay and integration of the underlying processes of perception, communication, computation, decision, action, and feedback, etc. This survey establishes the mobile embodied agent network (MEAN) as a task-oriented networking paradigm in which networking and distributed intelligence are jointly organized around closed-loop embodied task execution. We first establish a unified MEAN framework that explains how network resources and intelligent functions jointly support embodied task execution, and extend its performance indicators from conventional communication metrics to embodied task outcomes

ALT We study power allocation for a multiuser integrated sensing and communication (ISAC) downlink with bursty queues and imperfect channel state information (CSI). Starting from a long-run utility–backlog objective, we derive its common task reward and online score. Integer departures are represented by threshold events of indefinite Gaussian quadratic forms, accounting for estimation error in both signal and interference. Their moments give the conditional one-slot task reward and characterize the error of an efficient plug-in score. An anchored quadratic Lyapunov look-ahead corrects its reward-greedy action with a bounded nominal loss. Across five CSI-error levels, the rule reduces mean queue exceedance by 27.4–43.9% (0.49–0.99 percentage points) relative to the plug-in maximizer, with mean reward loss below 0.01 per slot. The rule achieves higher reward and lower exceedance than DPP, SAC and PPO; its advantage over freshly retrained learning baselines extends to five configurations wit

ALT Classifying disaster-related social-media posts into humanitarian information labels is challenging because individual messages are typically short and context-limited, humanitarian categories can overlap semantically, and the CrisisBench label distribution is strongly imbalanced. This study investigates whether lightweight lexical machine-learning methods can provide effective and computationally efficient classification under these conditions. Using the CrisisBench v1.0 English humanitarian task, comprising 87,455 records across predefined Train, Development and Test splits, we conduct a controlled comparison of TF-IDF representations and lightweight classifiers, with Macro-F1 as the primary model-selection metric. Beyond aggregate predictive performance, the study jointly examines per-label behaviour, directional error patterns, relative decision margins, computational requirements, and the effect of domain-specific training through a tropical-cyclone specialist model. The selected

ALT The deployment of Active Queue Management (AQM) policies is known to be a promising solution to bufferbloat, the problem of persistently-full large buffers in Internet routers. However, most AQMs involve tunable parameters, and some require awareness of the characteristics of the transport-layer protocol implemented at the end systems, thereby rendering their deployment infeasible. We consider one such AQM– a threshold-based queue policy which requires the packet-dropping threshold to be co-designed with relevant TCP parameters. We propose a mechanism that detects network congestion, estimates the congestion window evolution in the end hosts, estimates the required TCP parameter, and produces an optimal value of the packet-dropping threshold. Arguments based on analysis of the TCP fluid model are presented to substantiate the proposed mechanism. A simulation-based performance evaluation reveals the effectiveness of the proposed mechanism, compared to some widely implemented queue polic

ALT Future networks will seamlessly integrate computing and communication resources within a unified ecosystem of IoT devices, edge nodes, and cloud servers, forming an IoT-edge-cloud continuum. Within such an ecosystem, tasks can be dynamically offloaded to leverage resources across the continuum and efficiently scale with demand. Task offloading has traditionally focused on minimizing task execution latency or energy consumption. In contrast, we propose a deterministic task offloading and resource allocation scheme for the joint management of communication and computing resources to support services with deterministic service-level requirements. The proposed scheme aims to maximize the number of tasks completed on time, rather than minimizing the latency of individual tasks. To do so, it exploits variable task latency requirements to distribute the workload across the continuum. It can also prioritize high-priority tasks under resource scarcity. Numerical evaluations demonstrate that a d

ALT Intent-Based Networking (IBN) is a key enabler of autonomous network management in next-generation networks, as it allows operators to drive the network through high-level intents instead of low-level configuration commands. In this paper, we propose a semantic routing framework for telecom intent classification and translation across the radio access networks (RANs), transport, 5G core, and cloud/edge domains. This framework operates locally to preserve intent privacy and avoid the latency and cost of cloud-based Large Language Model (LLM) solutions. The proposed framework is composed of intent classification and translation. To accurately classify the intent, we propose fine-tuning sentence-transformer models using contrastive learning over a 3GPP TS 28.312-aligned taxonomy of five top-level routes and 22 branches, supported by a telecom-specific codebook of 1,100 intents. In addition, we develop a branchaware, template-driven rule-based module for intent translation that enforces de

ALT LLMs are becoming an indispensable part of network control systems due to their impressive reasoning. Before such LLM-enabled systems can be deployed, it is imperative to develop tools that can audit their performance on previously unseen inputs. However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems. It uses novel methods to extend deep reinforcement learning to guide the search for performance gaps and inputs that make LLMs struggle. LENS finds large gaps for LLM-based traffic engineering and GPU allocation systems, and even matches existing white-box analyzers designed for hand-crafted TE heuristics.

ALT Reliable indoor positioning remains a central unsolved problem in spatial computing. Existing systems depend on fixed, provider-owned infrastructure (WiFi access points, Bluetooth beacons, UWB anchors) whose deployment, calibration, and ownership models limit their scalability. Meanwhile, personal trackers, wearables, smart luggage tags, and tagged industrial assets are becoming densely embedded in the very indoor environments where positioning infrastructure is weakest. This paper argues that these devices point to a new class of spatial infrastructure: non-stationary, user-owned, and semantically interpretable. We present TrackerNet, a vision for transforming personal trackers from private object-finders into a decentralized substrate for ubiquitous positioning. The key shift is to stop treating anchor mobility as noise: TrackerNet models mobile anchors as probabilistic references whose stability, trust, and semantic role are inferred over time, reframing indoor localization as non-s

ALT LLM agents are widely used for software development and debugging, but their adoption in embedded systems, especially for wireless tasks, remains limited. This is because embedded development requires hardware-in-the-loop testing. Agents cannot directly observe the spectrum, probe pins, or measure power, so they cannot verify wireless link functionality; currently, human engineers perform this verification. We systematically evaluate whether LLM agents can establish wireless links when provided with hardware access. We introduce LoopHIL, a framework that lets agents control a multi-device RF bench, monitor it with a spectrum analyzer, logic analyzer, and energy meter, and validate links against a verification ladder, all within code-enforced safety limits. Across four increasingly complex tasks, from generating a carrier to backscatter decoding by a commercial receiver, and using six models from two vendors, hardware-in-the-loop increases task completion rates from 54% to 79% and from

ALT Classical stochastic geometry primarily addresses forward analysis by prescribing spatial point-process laws and mobility models. It does not directly address the inverse problem of recovering directional macroscopic transport from aggregate density evolution in non-stationary spatial networks. This paper develops the kinematic foundation of fluid-spatiotemporal stochastic geometry (F-STSG), representing an evolving mobile population by an intensity field under an assumed continuum limit while retaining the network infrastructure as a discrete spatial measure. We formulate macroscopic transport reconstruction as an inverse boundary value problem. Under a strictly positive bounded intensity and compatible source and boundary data, an instantaneous weighted minimum-energy criterion selects a unique velocity representative from the admissible class. The reconstruction reduces to a weighted elliptic Neumann problem and separates density-identifiable transport from weighted-solenoidal circu

ALT An agentic AI system for automated troubleshooting in optical communication networks is demonstrated that integrates data references into the agent architecture to prevent data hallucinations, improving the interpretability and trustworthiness of root-cause identification and reasoning.

ALT Multi-dwelling-unit (MDU) properties increasingly deploy one community-wide SSID on which every unit or resident has its own pre-shared key, mapped to its own VLAN so that each unit is a separate network. Vendors name this capability differently (ePSK, iPSK, MPSK, DPSK, PPSK); we call it multi-PSK (mPSK). When the keys are held in an external RADIUS server or a cloud controller, the access point (AP) must ask the key server to identify a client's key. Enabling IEEE 802.11r Fast BSS Transition with WPA2-PSK (FT-PSK), which large properties need for roaming voice and video, breaks this exchange in three ways. First, the server must identify the key from an FT handshake whose MIC depends on the FT key hierarchy. Second, the RADIUS messages no longer fit: EAPOL-Key message 2 and the key material the AP needs both exceed the 247-octet value capacity of a RADIUS vendor-specific attribute (VSA). Third, the AP a device roams to needs a PMK-R1 for that device, and all key material must disappea

ALT AI-RAN is driving the convergence of radio access network (RAN) processing and artificial intelligence (AI) workloads on GPU-enabled computing platforms, ranging from GPU-accelerated base stations to cloud-native infrastructures. While GPU acceleration improves the efficiency of virtualized RAN functions, the computational demand of latency-critical baseband processing varies with traffic load, creating transient spare GPU capacity that can be reclaimed by co-located AI applications. Existing GPU-sharing mechanisms rely on coarse-grained hardware partitioning or static resource allocation, limiting their ability to adapt to slot-level workload fluctuations while preserving strict timing requirements. This paper presents ScaleRAN, a node-local runtime for elastic GPU resource management in AI-RAN. ScaleRAN leverages NVIDIA Green Contexts to dynamically partition Streaming Multiprocessors (SMs) between RAN and AI workloads through a ladder of pre-created GPU partitions, enabling low-over

ALT Intelligent reflecting surfaces (IRSs) can improve spectrum efficiency and interference control in cognitive radio networks (CRNs) by reconfiguring the propagation environment. This paper presents FMASAC, a federated multi-agent soft actor-critic framework for IRS-assisted CRNs under partial observability, in which distributed secondary users (SUs) jointly select their transmit power and the phase shifts of an assigned subset of IRS elements using only local observations. Primary user (PU) protection is incorporated through an interference-penalized reward, and SUs coordinate by periodically averaging model parameters at a federated server, without exchanging raw observations, channel state information, or replay data. We formulate the problem as a partially observable Markov decision process, specify the local learning and aggregation procedure, and characterize its per-round communication cost. Illustrative performance trends contrast FMASAC with centralized SAC, independent SAC, IRS

ALT Wireless Digital Twins are emerging as a key enabler for predictive network control in 6G Vehicle-to-Infrastructure (V2I) systems. However, closed-loop V2I DTs face two dual-stage communication bottlenecks arising from model parameter exchange for V2I DT reconstruction and multi-service V2I traffic delivery after each reconstruction. This paper proposes a closed-loop dual-stage End-to-End (E2E) latency optimization framework that integrates Compressed Personalized Federated Learning with a unified Large-Language-Model-guided Multi-Agent Reinforcement Learning approach. A CPFL-enabled decision-oriented V2I DT is first constructed by jointly predicting Path Loss (PL) and Quality-of-Experience (QoE)-oriented Key Performance Indicators (KPIs). Then the framework comprises two temporally coupled but latency-wise distinct optimization stages: Stage I minimizes the joint Uplink (UL) and Downlink (DL) E2E transmission latency of compressed shared backbone's parameters, and Stage II minimizes t

ALT This paper evaluates an archival Bluetooth Low Energy received signal strength indicator (RSSI) dataset collected for bin localization on an industrial shop floor. The installation used 24 fixed relays positioned on walls and Bluetooth beacons attached to bins, with each relay applying a Kalman filter to its own readings in firmware before publishing them. The primary survey produced 19,034 usable two-minute fingerprint samples across 90 known positions, with 23 relay measurements available as model features. We compare calibrated path-loss multilateration with two radio-map estimators: distance-weighted k-nearest neighbours and Random Forest regression. A random 70/30 row split measures recognition at surveyed positions, while a coordinate-grouped split reserves 27 entire positions for testing and measures interpolation to unseen positions. Random Forest achieved a median error of 0.66 m for surveyed positions, with 77.9% of estimates within 3 m and 96.3% within 10 m. For unseen posit

ALT A base station can build a channel knowledge map (CKM) from the uplink pilots of its terminals and sweep only the beams that the map ranks highest. A fault at a terminal or at the base station corrupts the measurements taken while it lasts, and the map must then forget them. Removing the whole terminal discards its good measurements, and the raw reports needed to remove samples are gone. We define the structured deletion unit (SDU), which names the terminals, rounds, grids, and bands to forget. We propose Structured Wiping with Envelope-bounded Execution Partitioning (method), which executes an SDU by clearing blocks of an aggregated ledger and re-sounding the affected grids. method merges pending demands so that one campaign serves several, and it bounds what the smallest SDU containing a group clears beyond the demands. We prove that a merged execution leaves no named measurement in the map, that demands differing only in their intervals are best merged in contiguous runs, and that t

ALT Channel State Information (CSI) captures fine-grained changes in Wi-Fi channel propagation and provides a foundation for device-free sensing applications such as activity recognition, localization, and occupancy detection. The development of such applications depends on an acquisition system that can handle high-rate CSI streams while remaining extensible to the requirements of individual experiments. VesperCSI is an open-source, GPL-3.0-licensed redesign of FeitCSI for CSI acquisition with Intel AX200 and AX210 Wi-Fi interfaces. It targets high-rate acquisition on amd64 and aarch64 Linux hosts using fixed-capacity preallocated object pools and separate capture and consumer threads. A loadable plugin interface supports experiment-specific monitoring, storage, and processing. We evaluate VesperCSI and FeitCSI on Raspberry Pi CM4 receivers in a burst-traffic software crossover. At a 600-MHz all-core frequency ceiling and a target rate of 2500 frames/s, VesperCSI achieves mean sequence re

ALT This paper investigates the resilience and self-healing of unmanned aerial vehicle swarm communication networks under severe network partitioning in complex and adversarial environments. To address limited global observability after damage, we develop a damage-state inference mechanism that enables the ground control station (GCS) to infer global failures from sparse local reports without transmitting location-sensitive coordinates. To recover multi-cluster partitioned topologies, we then propose AdDiff-GT, a physics-inspired graph Transformer that incorporates advection-diffusion dynamics into a node-supernode hybrid graph. Local message propagation captures intra-cluster structural relationships, while global attention models inter-cluster interactions. A weighted composite loss jointly considers connectivity restoration, maneuvering cost, motion consistency, inter-cluster coordination, and spatial preservation during model training. We further develop a meta-learning strategy for tr