SEARCH · Searcharxiv

Search Searcharxiv

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources

arXiv2026

SecDT: A Profile-Based Security Layer for TRDP Communications

The Train Real-time Data Protocol (TRDP) is widely used on rolling stock but it provides limited native support for cryptographic protection. Furthermore, the multicast traffic profile used in TRDP Process Data to exchange critical information between onboard subsystems makes the introduction of cryptographic protection a challenge. This paper presents a lightweight security layer for secure TRDP communication that implements a number of security profiles built around modern cryptographic algorithms. This additional layer relies on an On-board Key Management System (OKMS) for both security profile negotiation, dynamic key distribution and key lifecycle management. The security profiles allow for cryptographic agility and flexibility, ranging from simple authentication to Authenticated Encryption with Associated Data (AEAD) algorithms. The security profile negotiation procedure guarantees all TRDP End Devices (ED) on a common Communication ID (ComID) share the same security profile and can therefore process each other's messages. A prototype implementation based on mbedTLS and Arm Platform Security Architecture (PSA) was developed and evaluated. Experimental results demonstrate manageable overhead, suitable for the real-time and time-sensitive communication found on rolling stock.

cs.CR↗

arXiv2026

Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

Learning-based schedulers have been proposed to provide real-time user, target, and access point (AP) association in distributed cell-free integrated sensing and communication systems. In a typical approach, a graph neural network (GNN), trained on labels from a mixed-integer linear program, maps lightweight per-AP statistics to decisions on AP clustering, user and target scheduling, and mode selection in one forward pass. Such solutions assume that hard constraints, enforced only as soft training penalties, hold at inference, and that the self-reported statistics are truthful. Using our ASSENT algorithm as an example, we find that despite high $F_1$ scores, many solutions violate at least one hard constraint, demonstrating that high prediction accuracy does not ensure joint feasibility. Projecting the GNN output onto a feasible solution restores constraint satisfaction with low utility loss, even with a simple greedy repair procedure. We further show that feasibility alone does not guarantee robustness to false data injection attacks. A single malicious AP that reports false information cannot substantially increase its user associations, but can greatly increase the rate of infeasible solutions. The effect of such attacks depends on the type of information being falsified. Misreporting information that affects the objective can largely be mitigated through feasibility projection, whereas falsifying information that affects the constraints cannot. The latter can, however, be detected using a low-complexity cross-AP consistency check. These results show that learned ISAC schedulers should be evaluated using constraint-aware feasibility metrics in addition to conventional accuracy measures.

cs.NI↗

arXiv2026

Connectivity of HAPS-Based Solutions for Large-Scale Wireless Networks: A Percolation Theory Analysis

In the era of sixth-generation (6G) wireless communication, numerous applications are expected to be realized, including environmental monitoring, smart agriculture, remote education, security protection, and intelligent transportation systems. These scenarios require large-scale, continuous Internet services in forests, rivers, oceans, and road networks, to name a few, where optical cables are difficult to deploy. High-altitude platform stations (HAPSs) emerge as a promising solution, offering low-latency, high-capacity services while facilitating the establishment of vertical heterogeneous networks (vHetNets) in fiber-less areas. This article investigates three HAPS-based solutions, where HAPSs can serve wireless devices directly or via gateway (GW) networks: the HAPS-to-device (H2D) scheme, the HAPS-to-GW-to-device (H2G2D) scheme, and the hybrid scheme. Leveraging percolation theory, we study the feasibility of large-scale continuous Internet coverage, where the key performance indicator (KPI) is the percolation probability. We discuss the subcritical and supercritical cases in different coverage schemes, and prove that the phase transition from zero to nonzero percolation probability appears when increasing the HAPS density or GW density. Numerical results verify that the curve of the critical condition of the phase transition exists between the derived lower bound and upper bound, which can help reduce the upfront cost of HAPS-based vHetNet solutions.

cs.NI↗

arXiv2026

Bringing dApps to OCUDU: An E3 Controller for Real-Time Open RAN Intelligence

Real-time control loops in Open RAN are increasingly colocated with the gNB, where dApps, i.e., programmable applications with sub-millisecond access to PHY- and MAC-layer signals, enable latency-critical use cases such as spectrum sharing, channel-aware scheduling, and integrated sensing. What turns dApps from a per-vendor mechanism into a portable component of the emerging AI-RAN ecosystem is the E3 interface: this interface defines how a dApp subscribes to RAN telemetry, receives indications, and issues control actions back to the RAN. To date, E3 has been implemented on OpenAirInterface and on NVIDIA Aerial; OCUDU, i.e., the Linux Foundation's open-source CU/DU project, has lacked a comparable E3 path. We close that gap with an open-source E3Controller for OCUDU, designed as a sidecar daemon so the E3 protocol stack, service-model logic, and dApp interactions live entirely outside RAN core processing, which is touched only through well-defined instrumentation hooks. We validate the controller with two reference service models, Spectrum and L1, that deliver O-RAN fronthaul I/Q samples to a spectrum-sensing dApp over a Foxconn RPQN 4800 radio unit. Our evaluation shows that the E3 path imposes no measurable throughput penalty on the OCUDU RAN, demonstrating that a real-time dApp/RAN interface is feasible on production-grade open RAN software.

cs.NI↗

arXiv2026

Theory of Network Wave

Aiming at the disorder problem (i.e. uncertainty problem) of the utilization of network resources commonly existing in multi-hop transmission networks, the paper proposes the idea and the corresponding supporting theory, i.e. theory of network wave, by constructing volatility information transmission mechanism between the sending nodes and their corresponding receiving nodes of a pair of paths (composed of two primary paths), so as to improve the orderliness of the utilization of network resources. It is proved that the maximum asymptotic throughput of a primary path depends on its intrinsic period, which in itself is equal to the intrinsic interference intensity of a primary path. Based on the proposed theory of network wave, an algorithm for the transmission of information blocks based on the intrinsic period of a primary path is proposed, which can maximize the asymptotic throughput of a primary path. In the cases of traversals with equal opportunities, an algorithm for the cooperative volatility transmission of information blocks in a pair of paths based on the set of maximum supporting elements is proposed. It is proved that the algorithm can maximize the asymptotic joint throughput of a pair of paths. As for the cases of traversals with unequal opportunities, an algorithm for the cooperative volatility transmission of information blocks in a pair of paths based on the set of maximum supporting elements is also proposed. The research results of the paper lay an ideological and theoretical foundation for further exploring more general methods that can improve the orderly utilization of network resources.

cs.NI↗

arXiv2026

Quantum Private Distributed Matrix Multiplication: Extending the Classical Codes and Limitations

In this paper, we explore how quantum resources can be used to increase the rate of private distributed matrix multiplication (PDMM). In PDMM, a user who has two high-dimensional matrices, A and B, and lacks the computational capabilities to apply matrix multiplication locally, divides the matrices A and B into K and L sub-blocks, respectively. Then, the user sends them to N servers to apply the required multiplication \emph{privately}, i.e., any $T$ colluding servers cannot get any information about the user's matrices. The goal is to reduce the number of servers needed to perform the required matrix multiplication, thereby decreasing the communication cost. First, in the high-privacy regime, the state-of-the-art classical code is called the gap additive secure polynomial (GASP) code. We define a feasibility requirement in the quantum setting for the GASP code such that the highest performance is achieved when the requirement is satisfied. Thus, super-dense coding gain is achieved when the feasibility condition is satisfied. We show that when $T \geq KL-K+1$, the feasibility condition is always satisfied and the GASP code can be extended to the quantum version. In the case of $T < KL-K+1$, the feasibility can still be satisfied. To further examine this behavior, we numerically study how the minimum privacy requirement depends on the matrix dimensions and provide a quadratic estimate for this relation. The results suggest that feasibility can be achieved when $T \sim 0.5 KL$. Second, in the low-privacy regime, the recently developed cyclic-addition degree tables (CAT) and discretely optimized GASP (DOG) codes are among the most efficient known classical constructions for PDMM. We show that the feasibility condition developed for GASP can be adopted for both CAT and DOG codes as well, thus unifying the feasibility framework for multiple classical PDMM coding schemes.

cs.IT↗

arXiv2026

Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States

\emph{Integrated communication and computation} (IC$^2$) has emerged as a new paradigm for enabling efficient edge inference in sixth-generation (6G) networks. However, the design of IC$^2$ technologies is hindered by the lack of a tractable theoretical framework for characterizing \emph{end-to-end} (E2E) inference performance. The metric is highly complicated as it needs to account for both channel distortion and artificial intelligence (AI) model architecture and computational complexity. In this work, we address this challenge by developing a tractable analytical model for E2E inference accuracy and leveraging it to design a \emph{channel-adaptive AI} algorithm that maximizes inference throughput, referred to as the edge processing rate (EPR), under latency and accuracy constraints. Specifically, we consider an edge inference system in which a server deploys a backbone model with early exit, which enables flexible computational complexity, to perform inference on data features transmitted by a mobile device. The proposed accuracy model characterizes high-dimensional feature distributions in the angular domain using a Mixture of von Mises (MvM) distribution. This leads to a desired closed-form expression for inference accuracy as a function of quantization bit-width and model traversal depth, which represents channel distortion and computational complexity, respectively. Building upon this accuracy model, we formulate and solve the EPR maximization problem under joint latency and accuracy constraints, leading to a channel-adaptive AI algorithm that achieves full IC$^2$ integration. The proposed algorithm jointly adapts transmit-side feature compression and receive-side model complexity according to channel conditions to maximize overall efficiency and inference throughput. Experimental results demonstrate its superior performance as compared with fixed-complexity counterparts.

cs.IT↗

arXiv2026

HALO: Hierarchical Auction-assisted Learning for Offloading in SAGIN

In this paper, we investigate delay-aware task offloading and resource scheduling in a three-tier space-air-ground integrated network (SAGIN) consisting of IoT devices, UAV edge nodes, and a high-altitude platform station (HAPS). We formulate joint task association and continuous resource control (including bandwidth, transmit power, and CPU frequency allocation) as a non-convex mixed-integer nonlinear programming (MINLP) problem, which is inherently NP-hard. To capture fine-grained system dynamics, we introduce a macro-micro slot model that tracks cumulative transmission and computation progress over time. Based on this model, we propose HALO, a hierarchical auction-assisted learning framework that combines auction-based task association with hierarchical Proximal Policy Optimization (HPPO) for resource allocation. Simulation results under different traffic loads show that HALO consistently outperforms representative deep reinforcement learning (DRL) baselines. In particular, HALO achieves an average improvement of 3.06 percentage points in task success rate over PPO (corresponding to a 3.4% relative gain) and shows consistently greater robustness than DDPG and SAC, with relative improvements of 10.6% and 4.8%, respectively. These results highlight HALO's ability to maintain stable and efficient performance under varying traffic conditions, making it well-suited for delay-sensitive SAGIN environments.

cs.NI↗

arXiv2026

Dynamic Modeling of Target Cell Location for Mobility Robustness Analysis in Cellular Networks: Technical Report

Mobility robustness optimization (MRO) requires an appropriate selection of handover (HO) parameters such as the time-to-trigger (TTT) and offset margin to balance HO failures and ping-pong HOs. Existing stochastic geometry-based analyses for MRO have treated the angular position of the target base station (BS) as uniformly distributed over a feasible region. However, this treatment does not explicitly capture the spatial distribution of the target BS dynamically selected as a user equipment (UE) moves through the network. In this paper, we develop a stochastic geometry-based analytical framework for MRO in sub-6 GHz cellular networks. We derive the distribution of the HO triggering time and the spatial distribution of the dynamically selected target BS under straight-line UE mobility. Based on these distributions, we formulate too-late HO and ping-pong HO events as mutually exclusive events and analytically derive their probabilities. Numerical results validate the analysis, demonstrate improved accuracy over the conventional uniform-angle model, and reveal the tradeoff between the two HO events and the dependence of the optimal TTT on BS density.

cs.NI↗

arXiv2026

MARS: Multipath Adaptive Reliable Service

Multipath transport is important for Internet/WAN services that move data volumes across heterogeneous paths, including geo-distributed analytics, content distribution, and cloud-service pipelines. Existing solutions face a trade-off: end-to-end transports such as MPTCP and MPQUIC are deployable but limited by endpoint-visible paths and delayed congestion feedback, while routing- or forwarder-assisted approaches often require infrastructure support or lack safe coordination across forwarding choices. This paper presents MARS, a receiver-driven, forwarder-assisted multipath transport. MARS combines tier-synchronized overlay path discovery with coupled consumer/forwarder congestion control, enabling it to expand usable forwarding opportunities and react near bottlenecks. It runs as an incrementally deployable UDP overlay at clients, servers, relays, or CDN-like nodes. We implement MARS in simulation and as a prototype, and evaluate it through simulation and Mininet emulation across deployment scopes, loss rates, and a forwarding-face outage scenario. Results show MARS provides deployment-dependent benefits: with endpoint-only deployment, it performs comparably to the evaluated ECMP-limited configurations of MPTCP and MPQUIC. With cooperating overlay forwarders, it expands the usable path set from routing-exposed forwarding candidates. Across the tested loss conditions, it reduces maximum T95 by up to 66.7% and 63.9% relative to the evaluated path-expanded MPTCP and MPQUIC configurations, respectively, given the same path set. Path discovery remains lightweight, flow fairness remains high, and MARS degrades gracefully during an emulated forwarding-face outage and recovers quickly after face restoration. Overall, ICN-style receiver-driven forwarding can serve as a deployable overlay transport substrate for coordinated WAN multipath without requiring changes to IP routing.

cs.NI↗

arXiv2026

Real-Time Neuromorphic Spectrum Intelligence Simulator

We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memristor-inspired agents for dynamic spectrum access under constrained energy budgets and adversarial conditions. RT-NuSIS couples leaky integrate-and-fire neuronal dynamics, memristive synaptic models, physics-informed energy-harvesting models (triboelectric and RF), and adversary models including jamming and Byzantine behavior. We formalize the simulator mathematically, prove boundedness, present a mean-field adversary threshold, analyze per-step complexity, and provide a reproducible benchmark harness for energy-per-inference, latency, and robustness metrics. The codebase is modular, deterministic by seed, and designed for large-scale event-driven simulations.

eess.SP↗

arXiv2026

Influence of Logging Frameworks on Bind9

Host-based Intrusion Prevention Systems (IPS) rely on application logs to detect and block malicious activity. However, on modern high-speed networks the logging subsystem itself becomes a bottleneck: an attacker can hide traces simply by generating enough traffic to overwhelm the application's log pipeline, dropping crucial traces. In this work, we show that widely deployed setups such as Fail2Ban monitoring BIND9 can be defeated with less than 65 Mbps of DNS traffic. Further, we show that when replacing core components of the IPS architecture with their higher-performance equivalent, iptables with eBPF and regex matching with Hyperscan, the logging backends themselves become the bottleneck. Therefore, we present FIPS, a new IPC designed for high-performance logging that bypasses the kernel and reduces copying of the log messages to a minimum. FIPS uses per-thread lock free shared memory ring buffers, supporting multiple independent consumers reading the same log stream at their own pace. FIPS offers both a native API and a drop-in replacement for the syslog interface. Our evaluation with BIND 9 shows that FIPS introduces almost no overhead compared to disabled logging, logs more requests than any other evaluated framework, and enables the IPS to ban malicious clients $2.5\times$ faster than with file logging while sustaining $2^{16}$ attacking clients at one million requests per second.

cs.CR↗

arXiv2026

Space Generative AI with Solar Energy Harvesting

Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by solar \emph{energy harvesting} (EH). This paper presents a framework for solar-powered space generative AI in which a satellite receives a user prompt, executes a diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental \emph{computation--communication} (C$^2$) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize \emph{end-to-end} (E2E) generative performance, we exploit the predictable solar-EH dynamics induced by deterministic orbital motion and develop a joint C$^2$ resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C$^2$ utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse solar-EH states.

cs.AI↗

arXiv2026

Lacan: Making Accountability in Anonymous Networks Real

Anonymity and accountability are essential properties for our everyday activity on the Internet. However, they appear contradictory, and their reconciliation remains far from reality. Existing approaches fall short in this regard, as they either rely on an on-path trustee, per-packet authorization, per-packet public-key cryptography, or per-session intervention by a central authority. We propose Lacan, a protocol that reconciles anonymity and accountability within a realistic design. In Lacan, a sender enjoys anonymity provided by on-path relays, as long as she complies with a contract established with the receiver. Upon a contract violation, the verifier, an off-path trustee on behalf of the receiver, links the malicious message to the sender's identity indirectly via the packet, path, and session, thereby reducing public-key operations from per-packet to per-session. This linkage remains robust even against malicious relays and receivers, grounded in our novel chain of successor proofs for accountable path reconstruction, together with traceable signatures, path validation, and key-committing encryption. We analyze the anonymity and accountability, implement the protocol, and evaluate the performance.

cs.NI↗

arXiv2026

Update for Decisions, Not Freshness: Goal-Oriented Status Updating and Selective Offloading at the Network Edge

In an edge--cloud collaborative edge-computing environment, an edge node (EN) must decide whether each user task should be executed locally, forwarded to a remote service (or cloud) node (SN), or rejected. The EN observes its local state directly but receives the SN state only through an intermittently refreshed cache. Status updating and task control therefore form an asynchronous closed loop under partial observability. Freshness-driven schemes, including those based on Age of Information (AoI), do not directly value an update by its effect on subsequent task decisions. We propose CoSMO (Co-design of Semantic-state Management and Offloading), a cooperative event-driven reinforcement learning (RL) framework that coordinates semantic status management and selective offloading through realized task utility. CoSMO learns a compact representation of the heterogeneous SN service state. At the SN, a recurrent semi-Markov double deep Q-network (Double DQN) agent jointly selects send/no-send and the next decision interval. At the EN, a task-terminal off-policy value-learning agent makes hierarchical gate--route decisions from local observations and stale remote semantics. The agents maintain separate observations and value targets but share the same realized task-utility stream, without centralized execution. Across the evaluated workload families, CoSMO's reported relative improvement in on-time completion rate over the best-performing competing method averages 18.6%--21.2%. For capacity-aware decision accuracy across the three strict-overload points, the corresponding reported gains average 17.6%--$17.9%.

cs.DC↗

arXiv2026

Sentinel-Based Failover for QKD-Augmented IPsec Tunnels

Quantum-safe IPsec through hybrid key establishment is practical, but creates a critical operational challenge: how to maintain tunnel availability when the QKD infrastructure becomes unavailable. In this paper, we present the design, implementation, and experimental evaluation of a quantum-safe key establishment mechanism for an IPsec tunnel that combines X25519, ML-KEM, and ETSI GS QKD 014 keys through the RFC 9370 multiple key exchange mechanism, and that degrades gracefully when the QKD key delivery fails. Our open-source StrongSwan plugin uses a sentinel-based coordination protocol, thereby permitting us to complete the handshake even if the QKD leg fails, instead of aborting, restoring the QKD share at the next rekey. On a testbed connected to a metropolitan QKD link over 33 km of deployed fiber, we evaluated five configurations, from a classical X25519 with RSA baseline to a hybrid one that adds ML-KEM-1024 and a QKD key. The full hybrid authentication costs 103 ms against 61 ms for the baseline, the QKD retrieval itself adds only about 7 ms. Failure injection experiments confirm that the tunnel survives a complete KME outage without any interruption of the protected traffic.

cs.NI↗

arXiv2026

TriSLA: A Preventive and Closed-Loop SLA-Aware Architecture for Multidomain Decision-Making with Explainable Artificial Intelligence in 5G Networks

Network slicing in multidomain 5G environments introduces critical challenges in guaranteeing Service Level Agreements (SLAs) under dynamic resource variability and heterogeneous service requirements. This article presents TriSLA, a closed-loop, preventive, SLA-aware architecture designed to evaluate feasibility at request time and continuously ensure SLA compliance during operation. The architecture combines ontology-driven semantic intent interpretation, multidomain machine learning feasibility risk inference, Explainable Artificial Intelligence (XAI) feature attribution, and closed-loop runtime SLA assurance into a unified operational pipeline. A fully operational prototype was evaluated in a multi-node cloud-native environment integrating Radio Access Network (RAN), Transport Network (TN), and 5G Core (5GC) domains with real-time telemetry collection. Experimental evaluation demonstrates that TriSLA guarantees a 100% SLA satisfaction rate for admitted slices, completely eliminating post-deployment violations compared to reactive (51.2%) and static threshold (80.4%) admission baselines. The predictive feasibility assessment achieved a classification accuracy of up to 99.51% (98.68% for the default explainable Random Forest classifier), enabling preventive admission decisions before infrastructure commitment. Furthermore, the cognitive admission pipeline introduces minimal processing overhead, requiring 25.37 ms for ontology-driven semantic parsing and 231.66 ms for XAI-assisted feasibility inference. Concurrently, the closed-loop assurance engine resolves 100% of runtime telemetry anomalies within a 4.22 s recovery cycle. These results demonstrate that TriSLA provides reliable, explainable, transparent, and preventive SLA management through integrated predictive admission and closed-loop runtime assurance for next-generation 5G networks.

cs.NI↗

arXiv2026

Contribution-Aware Bandwidth Allocation for Multimodal Split Learning

Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, because a client holding several sensor streams cannot host an encoder per modality. Split Learning makes such training feasible by keeping only the first layers on the device, at the cost of an uplink that must carry smashed activations for every modality at every step. Existing compression schemes give each modality the same keep-ratio, so the shared budget is divided in proportion to smashed-activation dimension, a quantity unrelated to how much each modality contributes to the fused prediction. We make that division an explicit decision and call it inter-modality allocation: under a fixed uplink budget, every policy transmits the same expected payload and differs only in how that payload is split across modalities. Our allocator, ModalShare, sets each modality's keep-ratio from a Shapley contribution score that the server computes over coalitions of activations it has already received. Measuring this score adds no uplink traffic and no client-side computation, and needs no prior knowledge of which stream is which. ModalShare improves accuracy over equal keep-ratios by 15.4 and 12.4 percentage points on CREMA-D and MVSA at matched payload in 5x compression, with strong performance across three compressors, three datasets, and four budgets. We show that existing compressors underperform in multimodal settings, with ModalShare recovering what gains are left behind.

cs.LG↗