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版本更新 · v2arXiv:2607.15593

Scaling LLM Agent Tool Access: The MCP Gateway

Mingxin Li, Enge Song, Yueshang Zuo, Xiaodong Liu, Rong Wen, Qiang Fu, Gianni Antichi, Jian He 等 30 位作者

原文摘要

LLM agents increasingly rely on tool calling to complete tasks. Model Context Protocol (MCP) standardizes tool calling between LLM agents and tool providers. It reduces integration cost and drives rapid growth in tool scale. In production deployments, tool access becomes difficult to scale. On the tool provider side, legacy services are not directly callable through MCP; the rapid protocol development creates ongoing compatibility cost. On the agent side, the number of mounted tools is limited by the LLM context window and inference overhead; mounting a large tool set increases token usage and inference latency and can reduce task success rate. Moreover, for stateful MCP backends with multiple replicas, preserving session affinity increases client-side complexity. We present a cloud-scale MCP Gateway system. It breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing at the gateway. The conversion module supports 315 OpenAPI services in the cloud and integrates MCP transport variants. Session-aware routing decouples the scalability of the gateway layer and the MCP service layer. The hybrid retrieval engine sustains $\sim$98\% Top-15 recall; it scales agent tool access to 3{,}000+ tools with improved tool selection accuracy. We also share the lessons learned from deploying the gateway system in production.

cs.DC · cs.AI · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03024

DisC: Incentive Design for Green, Robust, and Fair Client Self-Participation in OTA Federated Learning

Driss Choukri, Essaid Sabir, Abdelkrim Haqiq

原文摘要

Over-the-Air Federated Learning (OTA-FL) improves communication efficiency by exploiting waveform superposition over wireless Multiple-Access Channels (MAC). However, client heterogeneity, fading, energy constraints, and participation imbalance can degrade both fairness and aggregation accuracy. Existing Pareto-oriented fair OTA-FL schemes may still suffer from large aggregation distortion when highly weighted clients experience poor channel conditions. This paper proposes DisC, a game-theoretic distributed client selection framework for fairness-aware, channel-aware, and energy-efficient OTA-FL. Each client selects its participation level by maximizing a utility that captures Pareto fairness importance, learning relevance, OTA distortion, energy consumption, and delay. We show that DisC admits a Nash equilibrium and can be formulated as an exact potential game, where stable equilibria correspond to locally optimal OTA-FL operating points. DisC therefore offers a distributed, incentive-compatible alternative to centralized client selection under wireless and resource constraints.

cs.NI · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02913

Multipartite FSO/QKD with N-GHZ States for Scalable Secure Group Key Distribution

Yuma Kaminaga, Hoang D. Le, Cuong T. Nguyen, Anh T. Pham

原文摘要

The emergence of sixth-generation (6G) wireless networks is driving the need for secure group key generation mechanisms resilient to quantum-computing attacks. Conventional quantum key distribution (QKD) protocols are designed for point-to-point communication and are therefore not directly applicable to group key establishment. To address this limitation, this paper proposes a scalable High-Altitude Platform (HAP)-assisted multipartite QKD network architecture that integrates $N$-GHZ-state protocols with a Time Division Multiple Access (TDMA) scheme over free-space optical (FSO) channels. The performance of the proposed system is evaluated using a Qiskit-based simulation framework that incorporates realistic physical-layer impairments of FSO links. Simulation results demonstrate that the proposed architecture significantly improves the group secret key rate (SKR) compared to conventional bipartite approaches, while effectively reducing overall network execution delay. In addition, the analysis reveals the impact of channel impairments and provides practical design insights, such as optimal beam-waist control. The proposed framework demonstrates the potential of HAP-assisted multipartite QKD as an effective security solution for future large-scale 6G wireless networks supporting secure group communications.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.15856

Smart Interrupted Routing Based on Multi-head Attention Mask Mechanism-Driven MARL in Software-defined UASNs

Zhenyu Wang, Chuan Lin, Guangjie Han, Shengchao Zhu, Ruoyuan Wu, Tongwei Zhang, Jialu Tian

原文摘要

Routing-driven timely data collection in Underwater Acoustic Sensor Networks (UASNs) is crucial for marine environmental monitoring, disaster warning, and underwater resource exploration, etc. However, harsh underwater conditions, including high delays, limited bandwidth, and dynamic topologies, make efficient routing decisions challenging in UASNs. In this paper, we propose a smart interrupted routing scheme for UASNs to address dynamic underwater challenges. We first model underwater noise influences from real underwater routing features, e.g., turbulence and storms. We then propose a Software-Defined Networking (SDN)-based Interrupted Software-defined UASNs Reinforcement Learning (ISURL) framework, which ensures adaptive routing through dynamical failure handling (e.g., energy depletion of sensor nodes or link instability) and real-time interrupted recovery. Based on ISURL, we propose the MA-MAPPO algorithm, integrating multi-head attention mask mechanism with MAPPO to filter out infeasible actions and streamline training. Furthermore, to support interrupted data routing in UASNs, we introduce MA-MAPPO\_i, MA-MAPPO with interrupted policy, to enable smart interrupted routing decisions in UASNs. The evaluations demonstrate that our proposed routing scheme achieves exact underwater data routing decisions with faster convergence speed and lower routing delays than existing approaches.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.16092

Toward EU Sovereignty in Space: A Comparative Simulation Study of IRIS 2 and Starlink

Alexander Bonora, Marco Giordani, Michele Zorzi

原文摘要

The evolution of 6th generation (6G) networks increasingly relies on satellite-based Non-Terrestrial Networks (NTNs) to extend broadband connectivity to remote and unserved regions, and to support public safety. In this paper we compare two representative and conceptually different satellite constellation architectures, namely Starlink and IRIS 2. Starlink is a commercial private Internet constellation by SpaceX, based on dense Low Earth Orbit (LEO) satellites. It is primarily designed to deliver high-capacity broadband services for civil applications, with performance targets comparable to those of terrestrial networks. In contrast, IRIS 2 is a planned public initiative to be deployed by the European Union, based on a multi-layer combination of LEO, Medium Earth Orbit (MEO), and Geo-stationary Earth Orbit (GEO) satellites. It is primarily designed to provide a secure, resilient, and sovereign infrastructure for government and critical communications. After describing the main technical characteristics of Starlink and IRIS 2, we run a comprehensive simulation campaign to evaluate the design tradeoffs between the two. Specifically, we evaluate the per-cell and per-user achievable capacity, the impact of satellite mobility and handover, and identify the capability of each architecture to support global and reliable connectivity. We also provide design suggestions for possible future IRIS 2 deployment extensions.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.28369

Minimizing the Age of Incorrect Information of Discrete-Time Markov Sources With Hybrid ARQ

Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides, George Tzagkarakis

原文摘要

This work introduces a framework for analyzing the Age of Incorrect Information (AoII) in a real-time monitoring system with a generic discrete-time Markov source. We study a noisy communication system employing a hybrid automatic repeat request (HARQ) protocol, subject to a transmission rate constraint. The optimization problem is formulated as a constrained Markov decision process (CMDP), and it is shown that there exists an optimal policy that is a randomized mixture of two stationary policies. To overcome the intractability of computing the optimal stationary policies, we develop a multiple-threshold policy class where thresholds depend on the source, the receiver, and the packet count. By establishing a Markov renewal structure induced by threshold policies, we derive closed-form expressions for the long-term average AoII and transmission rate. The proposed policy is constructed via a relative value iteration algorithm that leverages the threshold structure to skip computations, combined with a bisection search to satisfy the rate constraint. To accommodate scenarios requiring lower computational complexity, we adapt the same technique to produce a simpler single-threshold policy that trades optimality for efficiency. Numerical experiments exhibit that both thresholdbased policies outperform periodic scheduling, with the multiplethreshold approach matching the performance of the globally optimal policy.

cs.IT · cs.NI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02662

S-R3: Secure and Robust RIS-Assisted RSMA under Finite-Resolution Phase Shifts

Ibrahima Sory Diallo, Elhadj Moustapha Diallo, Wang Mi, Mamadou Aliou Diallo, Amayika Kakati

原文摘要

This paper investigates a reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA) downlink with imperfect eavesdropper channel state information and finite-resolution RIS phases. The common stream carries public information, while user-specific private streams carry confidential information. For a conservative security assessment, each eavesdropper is allowed to remove the public stream and is given the other private streams as side information. We formulate a robust design of t he transmit precoders, isotropicartificial noise, common-rate allocations, and RIS phases that balances confidential and public throughput under rate and power constraints. Bounded errors in the direct and cascaded eavesdropper channels yield an effective-channel uncertainty bound valid across RIS phase updates. To address the resulting nonconvex problem, we construct conservative leakage bounds and convex inner transmit updates, initialize through a feasibility procedure, and search finite artificial-noise and RIS-phase candidates with explicit feasibility checks. For a fixed artificialnoise power and RIS configuration, the feasible inner iterates have a nondecreasing objective that converges in value, and their successive changes vanish. Accepted outer updates remain feasible and are finite in number. Our code is available at https://github.com/ABM-ADAM/RIS RSMA-secure.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24290

RxGS: Receiver-Generalizable 3D Gaussian Splatting for Radio-Frequency Data Synthesis

Kang Yang, Mani Srivastava

原文摘要

Radio-frequency (RF) data synthesis predicts the received signal given transmitter and receiver positions, and is essential for wireless applications. Recent 3D Gaussian Splatting (3DGS)-based methods achieve efficient synthesis at any transmitter location within a surveyed scene, but require a separate model for each receiver. We therefore present RxGS, a shared 3DGS model that first learns receiver-independent Gaussian geometry and then freezes it while learning directional radiance conditioned on receiver position. Its global conditioning branch captures receiver-dependent effects shared across the scene, while a local branch models per-scatterer variations from the receiver's geometry and occlusion. The shared geometry also supports batched rendering through a multi-receiver rasterizer. Across RF datasets, RxGS predicts at receiver positions excluded from training within the surveyed scene while significantly reducing training, inference, and storage costs. Project page: https://kangyangg.com/projects/rxgs/.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.15901

Scalable Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum

Keyvan Aghababaiyan, Baldomero CollPerales, Javier Gozalvez

原文摘要

Future 6 G networks are envisioned as a network of networks (NoN) ecosystem, integrating communication and computing resources across multiple domains. At the deep edge, IoT and end-user devices will form subnetworks for local communication and distributed task processing. These subnetworks will seamlessly integrate into the NoN ecosystem, creating an IoT-edge-cloud continuum. The unified resources across this continuum facilitate dynamic and scalable task offloading, unlocking new possibilities to support emerging services, including critical vertical services with stringent reliability and deterministic service level requirements. In this context, this paper demonstrates that a deterministic approach to task offloading and resource (communication and computing) allocation in the IoT-edge-cloud continuum not only ensures deterministic service levels but also enhances scalability compared to existing task offloading and resource allocation methods. By flexibly managing task completion deadlines while maintaining deterministic (i.e. bounded latency) service levels, deterministic policies achieve a more balanced workload and resource distribution across the continuum, ultimately improving scalability.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02157

Wireless Networks That See: Integrated Sensing and Communication in Complex Environments

Xingqin Lin

原文摘要

The same radio interactions that complicate wireless communication can reveal what is happening around a network. Integrated sensing and communication (ISAC) turns these interactions into opportunities to observe unmanned aerial vehicles (UAVs), road vehicles, and industrial mobile robots. Yet a strong echo is not necessarily a reliable object observation: stationary background can conceal motion, reflections can create virtual objects, and one viewpoint can leave part of an object's motion unobserved. This tutorial explains how known cellular signals are used to estimate propagation, suppress stationary background, interpret reflected paths, and combine observations from different sites. UAV examples under a Third Generation Partnership Project (3GPP) stochastic channel model and ray tracing in a San Francisco urban scene illustrate the mechanisms through spatial layouts, channel profiles, and before/after range--Doppler maps. Local checks reduce false outputs, with some loss of detection. Cooperation improves position accuracy for UAVs detected by both a single site and cooperating sites. The examples show how signal resources, measurement reporting, and application needs shape sensing performance, providing practical guidance for sensing services in complex environments.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02131

Band-Separated Redundant Communication for a Mars-Analog Rover

Zamiul Rashid, Akid Anis, Tanzim Ahmed, Md. Golam Rabiul Alam, Md. Khalilur Rhaman

原文摘要

Teams in the University Rover Challenge (URC) drive their rovers remotely and often out of sight. The organizers name communication failure as the most common reason a team falls short. A rover therefore needs bandwidth for several camera views and a command path that survives the loss of one radio. We describe and field test the communication stack of the BRACU Mongol-Tori rover, which keeps vision and control on separate radios and bands. Video and RTK corrections travel over a 5.2 GHz bridge with a 2.4 GHz backup, and the Babel routing protocol chooses between them. Every command goes out on two sub-gigahertz radios at once, and the rover uses the backup copy only when the primary falls silent. We tested the backhaul on the bench with a power cut and injected packet loss. We also drove the rover more than a kilometer from the base, and line of sight ended about halfway. On the bench, traffic reached the backup within a few seconds. A metric offset kept it on a primary that lost one packet in ten. In the field, the primary failed at 771 m and the backup carried video and corrections to just over a kilometer. The drive also exposed a limit of Babel, which judges a link only by lost Hello messages. It kept traffic on a primary with a faulty receiver, although the backup reported twice the capacity, and it flapped at the edge of range. We close with lessons on antenna aiming, link metrics and logging.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02123

Bounded Retained History in a Proxy Flow Replay: A Mixed Predictive Pilot and an Allocation Study

Juan S. Santillana

原文摘要

We examine bounded retained history through a frozen predictive pilot and a subsequent allocation study on a curated UNSW-NB15 flow mirror. Both use an artificial release-order replay based on raw ltime ticks, with equal-tick records querying a shared prior-group state. In the pilot, the model with added retained relational features had lower reconnaissance recall than the current-flow-plus-counters baseline (43.66% versus 47.84%) and lower benign false-positive rate (3.63% versus 6.68%) at separately frozen operating thresholds, together with lower AUROC. This mixed result did not establish a predictive advantage. A development-only audit subsequently found that capacity eviction dominated expiration in the 256-record, 300-tick FIFO state, with a median completed record lifetime of 11 ticks. A protocol specified before its single execution, but after the pilot and post-hoc mechanism audit, compared five label-blind retention arms on the already-used development trace. Its primary equal-source endpoint averaged retained/reference ratios for service-expanded source out-degree and source-destination service diversity. Source-balanced retention at 256 records exceeded FIFO-256 by a pooled endpoint difference of 0.440738 and met the prespecified materiality and subblock rule. Crucially, the event-weighted ratios for both quantities decreased relative to FIFO-256: the result is specific to the equal-source weighting objective and does not establish uniformly improved retention. The study supplies neither evidence of detection improvement nor an explanation of the pilot result. Reused data, concentrated reconnaissance support, proxy timing, and the absence of a qualified independent corpus bound the interpretation.

cs.CR · cs.LG · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02038

QueueTrack: Framework for Detecting the Onset of Queueing in Mobile Networks

Hamid Hassani, Anudeep Karnam, Kishor Joshi, Sonia M. Heemstra, Ignas G. Niemegeers, George Exarchakos

原文摘要

In mobile networks, timely and accurate detection of the onset of queueing at path bottlenecks remains challenging for end-to-end congestion control algorithms (CCAs), where end hosts have limited visibility into queue status. Traditional congestion signals, such as packet loss, round-trip time (RTT), and one-way delay (OWD), have well-known limitations for early queue buildup detection. This paper presents QueueTrack, a practical end-host framework to detect the onset of queueing in mobile networks. QueueTrack computes a queue buildup indicator, denoted QBI, from endpoint-observable packet-level timing statistics, without requiring tight synchronization between end hosts or modifications to the existing network infrastructure. We develop and assess the complete QueueTrack machine learning (ML) pipeline, including data collection, labeling, model training, and testing, on a 5G testbed built on the OpenAirInterface platform, under a range of scenarios with competing TCP traffic. The experimental results show that QueueTrack can reliably detect the onset of queueing at the 5G base station using low-complexity classification models such as logistic regression. For example, QueueTrack achieves an F1 score of approximately 80% in a challenging mixed TCP/UDP scenario. These findings confirm that QueueTrack can serve as a practical framework for developing CCAs that target low delay, as well as for network telemetry and network monitoring in mobile networks.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02032

PRISM: RL-Based Rate Control for Low Latency and High Data Rate in Mobile Edge Networks

Hamid Hassani, Staji Wilson, Kishor C. Joshi, Sonia M. Heemstra, Ignas G. Niemegeers, George Exarchakos

原文摘要

Emerging delay-sensitive interactive services, such as extended reality and remote operation, demand advanced congestion control algorithms (CCAs) capable of maintaining high data rates while keeping latency low in mobile networks. Existing CCAs typically rely on congestion signals such as packet loss, delay, or explicit congestion notification (ECN) marks, which can be delayed, imprecise, or require extensive upgrades to the network infrastructure. To address these limitations, physical resource block (PRB) utilization has recently been proposed as a reliable early congestion signal in mobile networks [1]. This paper introduces PRISM, a reinforcement-learning-based CCA that uses peak PRB utilization to regulate the send rate under challenging mobility and fading conditions. NS3 simulation results show that, in a mobility scenario where two PRISM flows coexist with two TCP Cubic flows and one TCP BBR flow, PRISM maintains a per-packet mean one-way delay (OWD) of 6.3ms and a $p_{99}$ OWD of 44.6ms, with negligible (0.02%) packet loss, over a 20min high-contention interval. However, over the same interval, the coexisting TCP flows experience 60x higher mean OWDs of 371-390ms and $p_{99}$ OWDs of 563-578ms, with 0.1% packet loss. PRISM achieves a 31x improvement in goodput-over-latency performance, indicating a favorable tradeoff for delay-sensitive applications.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01989

Accelerating QUIC Encryption in WebAssembly Using WASM-SIMD

Sota Yamawaki, Yasuo Okabe

原文摘要

WebAssembly (Wasm) runs untrusted code in a portable, lightweight sandbox and is increasingly used to host services on shared servers and edge platforms. Running the QUIC transport protocol inside Wasm would let each application choose and update its own transport without changing the host. QUIC, however, encrypts every packet, and Wasm offers neither dedicated AES instructions nor carry-less multiplication; a Wasm QUIC stack has been reported to reach only 17.6% of native goodput, with cryptography as its largest cost. Whether the 128-bit SIMD extension of Wasm (WASM-SIMD) can recover this cost, and what limits a pure-Wasm stack once it does, has remained unclear. Here we show that explicit WASM-SIMD kernels recover much of the cryptographic cost, after which non-cryptographic costs become the limit. On two hosts connected by 10-Gigabit Ethernet, kernels that process eight AES blocks or four ChaCha20 blocks per pass raise receiver goodput over a non-SIMD baseline by 61.34% for AES-128-GCM and 31.55% for ChaCha20-Poly1305, whereas compiler auto-vectorization gains only 1.28% for AES-128-GCM. ChaCha20-Poly1305 still delivers 1.99 times the goodput of AES-128-GCM, confirming end to end the preference that stand-alone cipher benchmarks suggest. Receiver-side profiling combined with Amdahl's law predicts that even native-speed cryptography would leave Wasm at roughly one third of the goodput of the fastest native stack. Cryptographic acceleration is therefore necessary but not sufficient: memory allocation, runtime overhead and per-datagram UDP I/O now bound pure-Wasm transports. We anticipate that this per-category cost breakdown will guide further optimization of Wasm network stacks and the design of system interfaces for batched I/O.

cs.CR · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16897

Beyond Callbacks: A Developer-friendly Programming Paradigm for Network Simulation

Yuanyi Zhu, Zijian Li, Xin Ai, Zixuan Chen, Sen Liu, Yang Xu

原文摘要

Network simulation plays a crucial role in both networking research and industry. Existing commonly-used Discrete Event Simulations (DES) are based on the callback mechanism for discrete events. However, as network functions grow in complexity, callbacks reveal a fundamental structural limitation: a callback can only represent a single point in a blocking operation and cannot capture the full operation as a coherent whole. This forces developers to manually decompose sequential network logic into scattered callback fragments, leading to stack ripping, callback hell, and heavy development and debugging burdens. We propose CoDES (\textbf{Co}routine-based \textbf{DES}), a coroutine-based programming paradigm for network simulation that addresses this structural limitation. CoDES targets development productivity rather than execution performance. Realizing CoDES in existing DES simulators is non-trivial, as coroutine suspension must be coordinated with the event scheduler, introducing challenges in coroutine resource conflicts, operation-level computation requirements, compatibility with existing callback-based simulators, and overhead. We implement a CoDES-based framework in ns-3, address all four challenges, and release it as open source. We evaluate CoDES by implementing Message Passing Interface (MPI), High Precision Congestion Control (HPCC), and Routing Information Protocol (RIP) in ns-3, spanning the transport, congestion control, and routing layers. Compared with callback-based counterparts, CoDES reduces lines of code by up to 51.4% and nesting depth by up to 50%, with no accuracy loss and negligible runtime and memory overhead.

cs.NI · cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01862

Interoperability Assessment of Fronthaul and E2 Interfaces for Multi-Vendor Open RAN Systems

Farhad Mehran, Charles Turyagyenda, Dritan Kaleshi

原文摘要

Open Radio Access Networks (RANs) aim to transform the existing wireless network architectures by adopting open interfaces, disaggregating and virtualising RAN components, and enabling dynamic control of the system via data-driven methods. To accelerate the development of interoperable RAN solutions, rigorous assessment of Open Fronthaul and E2 interfaces based on O-RAN Alliance Interoperability Testing (IOT) specifications plays a key role as the integration of these interfaces seems to be very challenging, e.g. due to the need for alignment of Fronthaul IOT profiles and unforeseen challenges for E2 interface integration as Near-Real-Time RAN Intelligent Controller (Near-RT RIC) is relatively a new component introduced within the RAN architecture. In this paper, selected parts of the latest findings and lessons learned from interoperability assessment of Open Fronthaul and E2 interfaces for 13 multi-vendor Open RAN systems deployed in SmartRAN Open Network Interoperability Centre (SONIC) Labs are presented.

cs.NI · eess.SP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.01465

Mobile Backscatter Communication for the Battery-less Internet of Things

Weining Song, Thiemo Voigt, Stefanos Kaxiras, Yuan Yao, Luca Mottola

原文摘要

We enable backscatter communication in the battery-less mobile Internet of Things (IoT). Backscatter communication is extensively studied in static settings. Existing designs are, however, fundamentally mismatched with mobility and time-varying energy patterns. Channel conditions rapidly fluctuate, impacting the achievable data rates and thus transmission costs. Energy availability varies unpredictably, possibly forcing devices to remain quiescent to recharge energy buffers. The two issues compound each other: while recharging, a battery-less mobile IoT device may miss more favorable channel conditions. We design a lightweight decision system that dynamically determines when to transmit by checking short-term trends in signal strength, while using Non-volatile Memory (NVM) to retain packets in unfavorable channel conditions and across energy failures. Using a prototype we built and real-world mobility and power traces, we compare our design against a rate-adaptive baseline that only considers the instantaneous channel conditions. Experimental results show that our system improves throughput by up to 5.16x while reducing transmission energy consumption by up to 47.3%, with only 0.23% - 7.3% additional energy overhead.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.02413

Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks

Po-Heng Chou, Chiapin Wang, Shou-Yu Chen, Hsiang-Ming Wang, Kuan-Fu Chen

原文摘要

This paper proposes a spatial-temporal learning-based routing framework for dynamic low Earth orbit (LEO) satellite networks. The routing problem is formulated as a partially observable Markov decision process (POMDP), where each satellite independently selects its next hop using network-state observations and destination information. A graph attention network (GAT) extracts spatial features from the dynamic satellite topology and queue states, while a long short-term memory (LSTM) network captures their temporal evolution. The resulting representation is integrated into a deep Q-network (DQN) for destination-aware next-hop selection without a separate traffic-prediction stage. Simulations in a 45-satellite LEO constellation under normal and bursty traffic demonstrate packet delivery performance comparable to a graph neural network (GNN)-enhanced routing baseline, although the proposed method incurs higher delay and queue occupancy. Ablation studies further demonstrate the complementary benefits of spatial and temporal learning.

cs.NI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01831

MATE: Multi-Agent Traffic Engineering for Topology-Aware Distributed SRv6 Scheduling under Bursty Traffic

Yuang Chen, Shuyi Liu, Chengdi Lu, Fengqian Guo, Hancheng Lu, Chang Wen Chen

原文摘要

As network traffic becomes increasingly dynamic and bursty, conventional centralized traffic engineering (TE) suffers from long control-loop delays caused by network-state aggregation, centralized computation, and policy distribution. In this paper, we propose MATE, a multi-agent TE method for proactive distributed scheduling in hybrid networks with partial deployment of Segment Routing over IPv6 (SRv6). MATE uses historical traffic stability to characterize the burst risk of each traffic demand and path sensitivity to quantify the ability of candidate paths to accommodate additional traffic, thereby enabling differentiated proactive scheduling tailored to heterogeneous traffic characteristics. MATE adopts an offline-centralized training and online-distributed execution architecture. During training, a shared global critic evaluates joint actions under a reward combining maximum link utilization (MLU) with variability-weighted path sensitivity, while counterfactual difference rewards provide agent-specific credit for actor learning. During online execution, edge agents generate traffic-splitting policies from local observations and apply them through SRv6, avoiding centralized optimization in each scheduling interval. Experiments on the China Environment for Network Innovations (CENI) testbed and simulations on the Sanet topology show that MATE outperforms a multi-agent reinforcement learning (MARL) ablation baseline without agent-specific credit assignment, COPE, and TEAL in most evaluated scenarios and approaches centralized FIGRET on measured traffic traces. On CENI-Abilene, MATE achieves MLU within 10% of the linear-programming optimum in up to 90.1% of test instances. MATE also achieves lower median normalized MLU than FIGRET in most tested failure scenarios, with mean neural-inference times of 2.05--3.14~ms.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01751

A Deep Reinforcement Learning Framework for Real-Time Electric Vehicle Charging Management in IoEV-Enabled Smart Cities

Nour-Eddine Moumni, Rachid Alaoui, Driss Kiouach

原文摘要

The rapid growth of electric vehicles (EVs) in urban environments demands intelligent, decentralised charging management strategies that operate under strict real-time constraints. This paper proposes an Edge-Intelligent architecture in which each EV selects a charging station (CS) by querying a shared, centrally trained Deep Q-Network (DQN) policy over a lightweight MQTT publish-subscribe backbone; independent per-EV edge inference is the deployment target, not the configuration evaluated here. The reported simulation evaluates that shared DQN policy applied sequentially to each vehicle's observations within a single SUMO run, using MQTT for KPI telemetry while observations are obtained through TraCI. The system is evaluated on two real-world road networks from OpenStreetMap (Rabat Agdal-Hay Riad and Tangier, Morocco); the primary network carries four geolocated public charging stations with heterogeneous power levels (50 kW and 150 kW). Three traffic demand profiles (morning, midday, evening peak) are simulated, and four decision policies are compared: an Adaptive Heuristic, a Tabular Q-Learning agent, the proposed DQN agent, and a Double DQN variant. A multi-seed statistical protocol (7 seeds, Wilcoxon signed-rank test, Cohen's d) evaluates the results. The DQN agent achieves a 6.8% relative improvement in charge rate and a 5.4% increase in delivered energy over the Adaptive Heuristic under Evening Peak demand ($p = 0.016$, Cohen's $d = 0.99$ and $1.51$), while showing no advantage in waiting time; on the second network the heuristic leads on waiting time, so the ranking is not stable across geographies. A scalability analysis shows linear state-space growth with station count and sub-millisecond inference; all runs include intermittent station outages under a Markov failure process.

cs.LG · cs.NI · eess.SY

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版本更新 · v2arXiv:2605.02180

Predictive Scheduling and Model Sharing for Continuous UAV Edge Inference

Houyi Qi, Minghui Liwang

原文摘要

Continuous edge inference for unmanned aerial vehicles (UAVs) faces uncertain wireless connectivity, competition for shared computing resources, and the overhead of model loading and session-state migration. These interdependent factors complicate request admission and the continuity of successive inference services. This paper investigates joint request admission, edge-server assignment, and model selection under an admission-priority objective that strictly favors any feasible increase in admitted requests over bounded losses in delay, interruption risk, model quality, migration, and deployment. To address this problem, we propose Service-Window-Guided Admission and Model Placement (SWAP), which integrates predictive service-window information with shared model deployment and coordinated scheduling. Quantile regression and probability calibration estimate the remaining service window and interruption risk, while a bounded residence factor characterizes the potential reuse of shared model instances. We then formulate a constrained exact potential game based on marginal-contribution utilities and develop a solution procedure combining unilateral improvement, coordinated admission, and finite binary logit refinement. Theoretical analysis establishes NP-hardness, the existence of a constrained pure Nash equilibrium, and finite descent, together with feasibility and approximate unilateral equilibrium guarantees for the returned assignment. Simulation results demonstrate improved service continuity and reduced request rejection under increasing resource contention, while characterizing the accompanying tradeoffs in model-deployment traffic, inference quality, and execution delay.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.09537

STEPS: Semantic Contract-Guided Scheduling for LLM-Assisted Natural Language-Driven Edge AI Services

Houyi Qi, Minghui Liwang, Xianbin Wang, Xinlei Yi, Seyyedali Hosseinalipour

原文摘要

Edge user/service scheduling governs where and how AI services are executed under limited communication and computing resources. Existing edge scheduling frameworks typically assume numerically specified service requirements, such as latency bounds and energy budgets. However, users often express ambiguous and context-dependent expectations in natural language, creating a gap between user intent and scheduling decisions. We propose semantic contract-guided edge potential scheduling (STEPS), a natural language-driven framework that introduces semantic contracts as executable interfaces between user-side semantics and edge-side decisions. A large language model (LLM)-assisted semantic parser extracts service requirements and raw confidence scores from natural language requests. Confidence calibration and interpretable mapping rules convert these outputs into preference weights, fulfillment bounds, and semantic uncertainty. STEPS formulates intended per-slot edge scheduling as an exact potential game for execution-node selection, computing-resource provisioning, and bandwidth allocation, followed by capacity-feasible resource realization. Fulfillment-driven feedback signals enable adaptive scheduling under evolving service and network conditions. We establish the existence of pure-strategy Nash equilibrium, prove finite-step convergence of uncapped strict best-response dynamics, and characterize the boundedness of projected adaptive-control parameters. Experiments under synthetic and Melbourne-based hybrid settings demonstrate improved contract fulfillment and reduced service loss relative to several adapted benchmarks. Post-drift evaluations further show that feedback adaptation improves fulfillment and admission coverage under the tested conditions.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01606

Exposure-Length Trade-off Optimization for Obstacle-Aware Intrusion Paths in Heterogeneous Wireless Sensor Networks

Nguyen Thi My Binh, Ho Viet Duc Luong, Nguyen Tuan Nghia, Nguyen Quang Huy, Dang Trong Hop

原文摘要

Barrier coverage is a key paradigm for intrusion detection and surveillance in IoT-enabled heterogeneous wireless sensor networks, where monitoring quality depends not only on the existence of sensing barriers but also on the detectability of feasible crossing paths. In this paper, we study the obstacle-aware maximal exposure path planning problem in heterogeneous sensor fields under truncated omni-directional sensing and line-of-sight occlusion. Unlike conventional maximal exposure formulations that focus only on exposure maximization, the considered problem seeks side-to-side feasible paths that simultaneously maximize accumulated sensing exposure and minimize path length in obstacle-constrained environments. To address this bi-objective problem, we develop OG-MOEA/D, a problem-specific MOEAD method that combines a fixed-grid path representation with obstacle-aware feasibility checking and adaptive repair. The standard decomposition, neighborhood update, SBX, and polynomial mutation mechanisms are inherited from MOEA/D rather than claimed as new. Extensive simulations on networks with varying sensor densities and obstacle layouts show that OG-MOEA/D consistently improves hypervolume over generations and produces well-distributed Pareto fronts. Direct obstacle-constrained comparisons with MOEA/D and NSGA-II over 20 independent runs show higher HV and lower IGD for OG-MOEA/D in all four tested sensor-network sizes. The results should be interpreted as controlled simulation evidence for surveillance-strength evaluation in synthetic two-dimensional heterogeneous WSN scenarios, rather than as a model of realistic evasive intrusion behavior or a complete real-world deployment.

cs.NE · cs.NI

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新论文 · v1arXiv:2611.01459

Vehicle and UAV Detection and Tracking with LTE Downlink Reference Signals: A Ray-Traced Evaluation on a Mapped Suburban Site with Licensed Base-Station Records

Arun K. Gurung, Satha K. Sathananthan

原文摘要

Passive sensing on the cellular downlink places the receiver at the user equipment and changes nothing in the network. We report simulation results for an LTE band-3 cell-specific reference signal arm on a ray-traced channel. On a synthetic street canyon the arm is compared with an uplink sounding reference signal arm on the same band, target and scorer: missed-detection probabilities of 0.667 and 0.772. On a surveyed site built from OpenStreetMap footprints and the licensed site record, nine receiver positions along the street track the vehicle at coverage between 29.9 % and 61.7 %; a 283 m lap separates target height from trajectory at 1.6 to 30 m; a 137 m lap on the built-up street tracks a vehicle and a 15 m airborne target on every cell, and 8 receivers at 135 m spacing carry one illuminator's coverage over 1140 m of the street. Potential future work includes extending the analysis to multiple illuminators and varying receiver configurations, and confirmation through over-the-air measurements.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01400

RadPose: ISAC-based RF 3D Human Pose Estimation

Geo Jie Zhou, Kunzhe Song, Ruxin Lin, Huacheng Zeng

原文摘要

Human pose estimation under adverse lighting and occlusion conditions remains challenging for camera- and LiDAR-based systems. Although mmWave radars provide robustness to illumination and occlusion, they rely on dedicated hardware and licensed spectrum, limiting scalability. In this paper, we present RadPose, a learning-based monostatic integrated sensing and communication (ISAC) framework that enables mmWave communication devices to perform 3D human pose estimation. Unlike specialized radars, RadPose leverages monostatic full-duplex operation to extract channel impulse responses (CIRs) directly from standard OFDM communication signals, generating 3D radio point clouds without additional sensing hardware. To address the sparsity and noise of RF observations, RadPose employs a cross-frame transformer-based pose inference network with semantic-guided spatial attention and target-driven temporal attention for robust spatio-temporal aggregation. It further adopts a joint-aware inverse kinematics refinement module based on graph convolution to enforce skeletal topology, bone-length consistency, and anatomical plausibility. Extensive experiments demonstrate RadPose achieves competitive accuracy compared to state-of-the-art radar-based approaches while maintaining robust performance in unseen and occluded scenarios. The dataset and source code are available at https://github.com/Geo-Chou/RadPose.

cs.CV · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01376

State-Augmented Learning for Post-Deployment Adaptation of Coexisting O-RAN Applications

Wonhyeok Ko, Yeonseo Jeong, Songnam Hong

原文摘要

Fixed control policies in Open RAN can require retraining when service requirements, traffic loads, or interacting applications change. We propose ATTUNE, a state-augmented learning method for an energy saving application that coexists with a learned mobility load balancing application under a service constraint. ATTUNE conditions the energy saving policy on a Lagrange multiplier and updates this scalar during operation using measured service degradation, while keeping both applications' network parameters fixed. To train a single policy across service requirements, ATTUNE samples target service levels uniformly and maps them to multipliers through an adaptive table. This procedure accounts for policy-dependent multiplier responses and improves energy savings compared with training over a widened fixed multiplier range. We characterize the induced distribution of service levels and the convergence and fluctuations of the online update. An exact finite-window identity establishes convergence of average measured degradation to the target when the projection corrections vanish on average. Simulations show that a single ATTUNE policy achieves energy savings comparable to policies trained separately for each service requirement. It adapts to changes in the target, load, and partner application without retraining, increases energy savings at light load, and maintains target tracking for separately trained application pairs. No statistically significant increase in cell switching is detected in the evaluated comparisons.

cs.LG · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01371

Token-as-a-Utility: A Model-Agnostic Architecture for Metering and Provisioning AI Inference Services

Yichiet Aun, Soung-Yue Liew, Yu Liang Lai, Jing Jie Tan, Dong Chen, Purnomo Husnul Khotimah

原文摘要

Token-based subscriptions are emerging as a commercial model for large language model inference, but extending them into interoperable utility services requires comparable consumption accounting. Raw token counts do not consistently measure inference resource use because demand varies with model architecture, context length, tokenisation, and concurrent serving conditions. This paper presents Token-as-a-Utility, a model-agnostic architecture integrating inference metering, service-level-aware provisioning, and inter-operator settlement. We prove that, under batched serving, no per-request meter can be additive, independent of co-resident traffic, and exactly cost-reflective at once. This motivates separating provisioned capacity, metered consumption, and contracted quality into distinct service products. Consumption is expressed in Reference Inference Units (RIUs) derived from prefill and decode demand, while occupancy-indexed operator ratings recover shared serving costs without altering the common unit. Versioned inference records support auditing and re-rating across operators; software-enforced capacity leases and quality-constrained routing enable provisioning on shared accelerators. The resource model is measured on three NVIDIA RTX 5000 Ada GPUs, and multi-tenant behaviour is evaluated with a trace-driven model calibrated to them. For the reference model, occupancy-indexed rating limits statement energy error to 9% under measured traffic, compared with up to 70.5% under fixed-occupancy rating. In calibrated multi-tenant experiments, tenant mispricing decreases from 7.36 times under flat token counting to 2.39 times with RIUs and 1.06 times after occupancy-based reconciliation. These findings demonstrate how portable inference accounting can be combined with load-dependent cost recovery and contract-based provisioning to support heterogeneous inference services across operators.

cs.DC · cs.NI · cs.PF

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.10083

Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations

Shayan Hamidi Dehshali, Tzu-Hsuan Liao, Shaileshh Bojja Venkatakrishnan

原文摘要

Low-Earth orbit (LEO) mega-constellations are emerging as high-capacity backbones for next-generation Internet. Deployment of laser terminals enables high-bandwidth, low-latency inter-satellite links (ISLs); however, their limited number, slow acquisition, and instability make forming a stable satellite topology difficult. Existing patterns like +Grid and Motif ignore regional traffic, ground station placement, and constellation geometry. Given sparse population distribution on Earth and the isolation of rural areas, traffic patterns are inherently non-uniform, providing an opportunity to orient inter-satellite links (ISLs) according to these traffic patterns. In this paper, we propose Starfield, a novel demand-aware satellite topology design heuristic algorithm supported by mathematical analysis. We first formulate a vector field on the constellation's shell according to traffic flows and define a corresponding Riemannian metric on the spherical manifold of the shell. The metric, combined with the spatial geometry, is used to assign a distance to each potential ISL, which we then aggregate over all demand flows to generate a score for each satellite's link selection. Inspired by +Grid, each satellite selects the link with the minimum Riemannian distance along with its corresponding angular links. To evaluate Starfield, we developed a custom, link-aware, and link-configurable packet-level simulator, comparing it against LAMP, +Grid, Motif, and Random topologies. For the Phase 1 Starlink, simulation results show up to a 30% reduction in hop count and a 15% improvement in stretch factor across multiple traffic distributions. Moreover, static Starfield, an inter-orbital link matching modification of Starfield, achieves a 15% improvement in stretch factor under realistic traffic patterns compared to +Grid. Experiments demonstrate Starfield's robustness under traffic demand perturbations.

cs.NI · cs.ET · math.DG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01212

Energy-Efficient Geographic Multicast Routing in Cognitive Wireless Sensor Networks

Farzan Heidari, Farzaneh Heidari

原文摘要

Energy consumption is a major design concern in wireless sensor networks. Cognitive radio provides opportunistic access to licensed spectrum, while multicast routing allows several destinations to share forwarding paths. In this paper, we propose two geographic multicast routing protocols for cognitive wireless sensor networks, CCast and PCast. CCast combines multicast-tree construction with a latency-oriented spectrum-selection metric. PCast uses a normalized link-power cost to select routes and can avoid regions where primary users occupy lower-frequency bands. Both protocols use local neighbor information and destination coordinates, without requiring a complete network-topology map. Simulations compare the proposed protocols with geographic multicast routing (GMP) and multiple unicast routing. In simulations, CCast and PCast reduce energy consumption by 21\% and 26\% relative to GMP, at the cost of longer per-destination routes and higher delivery delay.

cs.NI

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arXiv ↗PDF ↗