Networking and Internet Architecture

arXiv:cs.NI

Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards.

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Systems & Networking

Networking and Internet Architecture

arXiv:cs.NI

Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards.

Looking for a broader view? This category is part of:

Systems & Networking

Trending in Networking and Internet Architecture

MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability

We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified interface, supports integrating both native and third-party MAS via a repository of examples and lightweight adapters, and exports framework-agnostic execution traces together with system-level signals (e.g., latency, cost, and failures). We instantiate MAESTRO with 12 representative MAS spanning popular agentic frameworks and interaction patterns, and conduct controlled experiments across repeated runs, backend models, and tool configurations. Our case studies show that MAS executions can be structurally stable yet temporally variable, leading to substantial run-to-run variance in performance and reliability. We further find that MAS architecture is the dominant driver of resource profiles, reproducibility, and cost-latency-accuracy trade-off, often outweighing changes in backend models or tool settings. Overall, MAESTRO enables systematic evaluation and provides empirical guidance for designing and optimizing agentic systems.

2601.00481

Jan 2026Networking and Internet Architecture

Related categories:

Distributed, Parallel, and Cluster Computing Cryptography and Security Performance Systems and Control Information Theory

3,261 papers

Analyzing Symbolic Properties for DRL Agents in Systems and Networking

Deep reinforcement learning (DRL) has shown remarkable performance on complex control problems in systems and networking, including adaptive video streaming, wireless resource management, and congestion control. For safe deployment, however, it is critical to reason about how agents behave across the range of system states they encounter in practice. Existing verification-based methods in this domain primarily focus on point properties, defined around fixed input states, which offer limited coverage and require substantial manual effort to identify relevant input-output pairs for analysis. In this paper, we study symbolic properties, that specify expected behavior over ranges of input states, for DRL agents in systems and networking. We present a generic formulation for symbolic properties, with monotonicity and robustness as concrete examples, and show how they can be analyzed using existing DNN verification engines. Our approach encodes symbolic properties as comparisons between related executions of the same policy and decomposes them into practically tractable sub-properties. These techniques serve as practical enablers for applying existing verification tools to symbolic analysis. Using our framework, diffRL, we conduct an extensive empirical study across three DRL-based control systems, adaptive video streaming, wireless resource management, and congestion control. Through these case studies, we analyze symbolic properties over broad input ranges, examine how property satisfaction evolves during training, study the impact of model size on verifiability, and compare multiple verification backends. Our results show that symbolic properties provide substantially broader coverage than point properties and can uncover non-obvious, operationally meaningful counterexamples, while also revealing practical solver trade-offs and limitations.

2604.04914Apr 2026

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nascTime: A Full-Stack 5G-TSN Bridge Simulation Framework with SDAP-Based QoS Mapping and IEEE 802.1AS Transparent Clock

The integration of 5G with IEEE 802.1 Time-Sensitive Networking (TSN) is essential for enabling flexible and mobile deterministic communication in industrial automation. The 3GPP Release 16 specification defines a bridge architecture where the 5G system operates as a transparent TSN bridge, incorporating Network-side and Device-side TSN Translators (NW-TT, DS-TT), a TSN Application Function, and QoS mapping between TSN Priority Code Points and 5G QoS Flow Identifiers. However, existing simulation frameworks model only subsets of this architecture, either QoS mapping without time synchronization, or time synchronization without data plane traffic, and none implements the complete QoS pipeline through the 3GPP SDAP layer with per-flow Data Radio Bearer selection. We present nascTime[20], an open simulation framework built on OMNeT 6.3, INET 4.6, and Simu5G that implements the complete 3GPP Release 16 5G-TSN bridge model. The framework provides end-to-end QoS mapping from TSN PCP through to 5G QFI via the SDAP/DRB pipeline, IEEE 802.1AS transparent clock behavior with measured residence time correction through L2-in-GTP-U gPTP transport, and multi-endpoint scaling with bidirectional traffic. The bridge ports integrate with INET's LayeredEthernetInterface and streaming PHY for compatibility with TSN features including Time-Aware Shaping and frame preemption. We validate nascTime with a three-endpoint factory scenario demonstrating near-perfect packet delivery across two traffic classes, correct gPTP synchronization with residence time correction, and zero packet loss. nascTime is the first simulation framework to model the full 5G-TSN bridge data path with SDAP-based QoS differentiation and measured IEEE 802.1AS transparent clock behavior in a multi-endpoint topology.

2604.04616Apr 2026

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Comprehensive Analysis of Cellular Uplink Performance in a Dense Stadium Deployment

Uplink performance remains a critical limitation in modern 5G networks, where UEs have to balance limited transmission power against propagation challenges. We conducted extensive measurements in the University of Notre Dame's football stadium, which has a seating capacity of 80,000 spectators, evaluating network behavior under both unloaded (pregame) and severely congested (game day) conditions, with a focus on uplink performance. Analyzing PHY-layer metrics captured via the Rohde & Schwarz QualiPoc, we show that high-frequency TDD bands in the uplink are severely bottlenecked in both the spectral and temporal domains. Despite transmitting near maximum 3GPP power limits, propagation loss inherent to high-frequency bands restricts UEs to low MCS indices and low PRB allocations, even in unloaded networks. This inability to achieve wideband allocation is further compounded by the significantly smaller number of uplink slots compared to downlink slots in TDD frames. Consequently, we observe a severe disparity between uplink and downlink: while high-frequency TDD bands carry the majority of downlink throughput, the network relies heavily on lower-frequency FDD bands for uplink. Additional measurements under favorable propagation conditions around a Verizon COW deployment located in the stadium parking lot also show that this limitation is not solely propagation-driven; rather, the duplexing scheme itself also plays a significant role. Even when TDD bands achieve higher or comparable MCS, FDD bands have a performance edge in the uplink due to the restrictive, downlink-heavy TDD architecture. These findings emphasize the indispensable role of low-frequency FDD spectrum in sustaining uplink capacity, providing insights that will help guide the design of next-generation wireless networks.

2604.04371Apr 2026

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OrbitTransit: Traffic Delivery and Diffusion for Earth Observation via Satellite Mobility

The emerging demand for Earth observation (EO) to address environmental challenges has driven unprecedented growth in its primary carrier, Low Earth Orbit satellites, in recent years. Ground stations (GSs), the egress points of these networks, are congested due to the massive volume of EO traffic, and their deployment is constrained by geographic, political, and budgetary factors. Although inter-satellite links (ISLs) can partially relieve this congestion by forwarding traffic to alternative GSs, existing ISL-based approaches can hardly address traffic contention caused by biased GS distribution and may also raise sustainability concerns due to prolonged ISL paths. In this paper, we propose OrbitTransit, a pickup-carry-offload (PCO) approach that leverages satellite mobility for data \textit{delivery} and integrates ISLs for traffic \textit{diffusion} to alleviate the resource contention inherent in PCO delivery. The proposed orbit-as-node framework and contention-avoidant delivery jointly determine the optimal hybrid PCO-ISL path, minimizing energy consumption and balancing GS traffic. Extensive experiments show that OrbitTransit reduces battery consumption by $47.16\%$, decreases task failures by $1.09\times$, and improves GS load balancing compared with state-of-the-art GS selection and routing algorithms.

2604.04368Apr 2026

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A Family of Open Time-Series Foundation Models for the Radio Access Network

The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop control. However, current RAN intelligence is still largely built from task-specific models tailored to individual functions, resulting in model fragmentation, limited knowledge sharing across tasks, poor generalization, and increased system complexity. To address these limitations, we introduce TimeRAN, a unified multi-task learning framework for time-series modeling in the RAN. TimeRAN leverages a lightweight time-series foundation model with few task-specific heads to learn transferable representations that can be efficiently adapted across diverse tasks with limited supervision. To enable large-scale pretraining, we further curate and open-source TimeRAN DataPile, the largest time-series corpus for RAN analytics to date, comprising over 355K time series and 0.56B measurements across diverse telemetry sources, protocol layers, and deployment scenarios. We evaluate TimeRAN across a comprehensive set of RAN analytics tasks, including anomaly detection, classification, forecasting, and imputation, and show that it achieves state-of-the-art performance with minimal or no task-specific fine-tuning. Finally, we integrate TimeRAN into a proof-of-concept 5G testbed and demonstrate that it operates efficiently with limited resource requirements in real-world scenarios.

2604.04271Apr 2026

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RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge

Federated learning (FL) over heterogeneous IoT edge devices faces coupled system-modality-data heterogeneity: the lower-cost device carries both fewer sensors and less computational power, so the slowest device (straggler) produces the most incomplete gradient signals. Naively averaging their updates dilutes rare-modality information and wastes computation on absent-sensor parameters, whereas existing methods handle the triple heterogeneity (system, modality, data) in isolation and none addresses their coupling. To resolve this issue, we propose RELIEF, a framework that partitions the fusion-layer Low-Rank Adaptation (LoRA) projection matrix into modality-aligned column blocks and uses this partition as a unified interface for aggregation, elastic training, and communication. Each block is aggregated only within the cohort of devices possessing that modality, which eliminates cross-modal gradient interference; the server then allocates personalized training budgets by prioritizing blocks with the highest cohort-internal divergence, so that resource-constrained devices train fewer but more impactful parameters. We prove that cohort-wise aggregation removes interference from the convergence bound and that the divergence-guided allocation achieves sublinear regret. Experiments on two IoT sensor datasets (PAMAP2, MHEALTH) under both full-parameter (CNN) and parameter-efficient (LoRA) training show that RELIEF achieves up to 9.41x speedup and 37% energy reduction over FedAvg with up to 15.3 pp rare-modality F1 gains, and real-device validation on a two-Jetson AGX Orin testbed confirms these results.

2604.04243Apr 2026

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Advanced Holographic Multi-Antenna Solutions for Global Non-Terrestrial Network Integration in IMT-2030 Systems

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity across terrestrial and non-terrestrial domains. This will be possible by integrating non-terrestrial networks (NTNs) to extend coverage to underserved areas. Antennas are central to this vision, with multiple-input multiple-output (MIMO) technologies receiving the most attention due to their ability to exploit spatial multiplexing to improve link capacity and reliability. However, conventional MIMO can consume significant energy, as each antenna element typically requires an independent RF chain. This limitation is particularly critical in non-terrestrial systems, where onboard energy resources are limited. Holographic MIMO (HMIMO) has emerged as a promising alternative in this context. These systems are based on theoretically continuous apertures, where radiation is generated through controlled modulation of surface impedance. This enables beamforming mechanisms with significantly fewer RF chains, reducing power consumption. In this work, we make the case for HMIMO as a suitable candidate for NTN integration within IMT-2030 systems. We discuss its advantages over conventional MIMO and present a case study of HMIMO integration in LEO-based multi-user communication.

2604.04149Apr 2026

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UAV Control and Communication Enabled Low-Altitude Economy: Challenges, Resilient Architecture and Co-design Strategies

The emerging low-altitude economy has catalyzed the large-scale deployment of unmanned aerial vehicles (UAVs), driving a paradigm shift in environment monitoring, logistics, and emergency response. However, operating within these environments presents notable challenges as pervasive coverage holes, unpredictable interference, and spectrum scarcity. To this end, this article present a communication and control co-design framework to enable a resilient architecture for cellular-connected UAVs. Specifically, we first characterize typical service applications and their stringent performance requirements, followed by a comprehensive analysis of the unique challenges. To bridge the gap between volatile wireless links and rigid flight stability, a three layered architecture is proposed, integrating pre-flight strategic planning, in-flight adaptive action, and system-level resource orchestration. Furthermore, we detail the key enabling technologies for communication and control co-design. Preliminary case studies are proposed to validate that the co-design framework significantly improve the resilience of cellular-connected UAV systems, providing a robust foundation for the evolution of intelligent low-altitude networks.

2604.04044Apr 2026

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Reimagining RAN Automation in 6G: An Agentic AI Framework with Hierarchical Online Decision Transformer

In this paper, we propose an Agentic Artificial Intelligence (AI) framework for wireless networks. The framework coordinates a pool of AI agents guided by Natural Language (NL) inputs from a human operator. At its core, the super agent is powered by a Hierarchical Online Decision Transformer (H-ODT). It orchestrates three categories of agents: (i) inter-slice, intra-slice resource allocation agents, (ii) network application orchestration agents, and (iii) self-healing agents. The orchestration takes place with the help of an Agentic Retrieval-Augmented Generation (RAG) module that integrates knowledge from heterogeneous sources. In this proposed methodology, the super agent directly interfaces with operators and generates sequential policies to activate relevant agents. The proposed framework is evaluated against three state-of-the-art baselines, showing improved throughput, reduced network delay, and higher energy efficiency at both slice-level and system-wide performance metrics. Also, the proposed Agentic framework introduces a bi-level human operator intent validation methodology, both at the slice-level and Key Performance Indicator (KPI)-level using generative AI-based time series predictors. We could rule out performance-degrading operator intents with an accuracy of 88.5%. Lastly, while being interrupted by any performance-degrading events, the self-healing capability of Agentic AI in our framework automatically recovers 90% of its previous performance, avoiding quality-of-service drifts when there is no human involvement.

2604.03908Apr 2026

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CCA Reimagined: An Exploratory Study of Large Language Models for Congestion Control

In this paper, we conduct an emulation-guided study to systematically investigate the feasibility of Large language model (LLM)-driven congestion control. The exploration is structured into two phases. The first phase derisks the whole capability where we isolate the role of LLM on a single yet crucial congestion avoidance phase so that we can safely examine when to invoke the LLM, what information to provide, and how to formulate LLM instructions. Based on the gained insights, we extend LLM's role to multiple congestion control phase and propose a more generic LLM-based congestion control policy. Our evaluation on both static and dynamic network traces demonstrates that the LLM-based solution can reduce latency by up to 50\% with only marginal throughput sacrifice (e.g., less than 0.3\%) compared to traditional CCAs. Overall, our exploration study confirms the potential of LLMs for adaptive and general congestion control, demonstrating that when granted appropriate control freedom and paired with an effective triggering mechanism, LLM-based policies achieve significant performance gains, particularly under highly dynamic network conditions.

2604.03857Apr 2026

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CB-VER: A Stable Foundation for Modular Control Plane Verification

Network operators are often interested in verifying \emph{eventually-stable properties} of network control planes: properties of control plane states that hold eventually, and hold forever thereafter, provided the operating environment remains unchanged. Examples include eventually-stable reachability, access control, or path length properties. In this work, we introduce \textsc{CB-Ver}, a new framework for verifying such properties, based on the key idea of a \emph{converges-before graph} (CB-graph for short). When a user provides interfaces for each network component, \textsc{CB-Ver} checks the necessary component-by-component requirements in parallel using an SMT solver. In addition, the tool automatically synthesizes a CB-graph and checks whether it connects all nodes in a network -- if it does, the interfaces are valid and users can check whether additional eventually-stable properties are implied. Moreover, the CB-graph can then be used to determine fault tolerance properties of the network. We formalize our verification algorithm in the Lean theorem proving environment and prove its soundness. We evaluate the performance of \textsc{CB-Ver} on a range of benchmarks that demonstrate its ability to verify expressive properties in reasonable time. Finally, we demonstrate it is possible to automatically generate suitable interfaces by turning the problem around: Given a CB-graph, we use an off-the-shelf Constrained Horn Clause (CHC) solver to synthesize interfaces for every network component that together ensure the given correctness property.

2604.03539Apr 2026

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Causal Inference for Quantifying Noisy Neighbor Effects in Multi-Tenant Cloud Environments

Resource sharing in multi-tenant cloud environments enables cost efficiency but introduces the Noisy Neighbor problem, i.e., co-located workloads that unpredictably degrade each other's performance. Despite extensive research on detecting such effects, there are no explainable methodologies for quantifying the severity of impact and establishing causal relationships among tenants. We propose an analytical that combines controlled experimentation with multi-stage causal inference and validates it across 10 independent rounds in a Kubernetes testbed. Our methodology not only quantifies severe performance degradations (e.g., up to 67\% in I/O-bound workloads under combined stress) but also statistically establishes causality through Granger causality analysis, revealing a 75\% increase in causal links when the noisy neighbor activates. Furthermore, we identify unique "degradation signatures" for each resource contention vector (i.e., CPU, memory, disk, network), enabling diagnostic capabilities that go beyond anomaly detection. This work transforms the Noisy Neighbor from an elusive problem into a quantifiable, diagnosable phenomenon, providing cloud operators with actionable insights for SLA management and smart resource allocation.

2604.03145Apr 2026

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RL-Loop: Reinforcement Learning-Driven Real-Time 5G Slice Control for Connected and Autonomous Mobility Services

Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation by over 55% relative to the reference operating point while reaching a comparable quality-of-service degradation region. These results indicate that lightweight reinforcement learning--based feedback control can provide efficient and responsive resource management for 5G-enabled smart mobility and connected vehicle services.

2604.02461Apr 2026

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CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC's resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.

2604.02284Apr 2026

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Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

Wideband channel frequency response (CFR) estimation is challenging in multi-band wireless systems, especially when one or more sub-bands are temporarily blocked by co-channel interference. We present a physics-informed complex Transformer that reconstructs the full wideband CFR from such fragmented, partially observed spectrum snapshots. The interference pattern in each sub-band is modeled as an independent two-state discrete-time Markov chain, capturing realistic bursty occupancy behavior. Our model operates on the joint time-frequency grid of $T$ snapshots and $F$ frequency bins and uses a factored self-attention mechanism that separately attends along both axes, reducing the computational complexity to $O(TF^2 + FT^2)$. Complex-valued inputs and outputs are processed through a holomorphic linear layer that preserves phase relationships. Training uses a composite physics-informed loss combining spectral fidelity, power delay profile (PDP) reconstruction, channel impulse response (CIR) sparsity, and temporal smoothness. Mobility effects are incorporated through per-sample velocity randomization, enabling generalization across different mobility regimes. Evaluation against three classical baselines, namely, last-observation-carry-forward, zero-fill, and cubic-spline interpolation, shows that our approach achieves the highest PDP similarity with respect to the ground truth, reaching $ρ\geq 0.82$ compared to $ρ\geq 0.62$ for the best baseline at interference occupancy levels up to 50%. Furthermore, the model degrades smoothly across the full velocity range, consistently outperforming all other baselines.

2604.01944Apr 2026

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Quantum Networking Fundamentals: From Physical Protocols to Network Engineering

The realization of the Quantum Internet promises transformative capabilities in secure communication, distributed quantum computing, and high-precision metrology. However, transitioning from laboratory experiments to a scalable, multi-tenant network utility introduces deep orchestration challenges. Current development is often siloed within physics communities, prioritizing hardware, while the classical networking community lacks architectural models to manage fragile quantum resources. This tutorial bridges this divide by providing a network-centric view of quantum networking. We dismantle idealized assumptions in current simulators to address the "simulation-reality gap," recasting them as explicit control-plane constraints. To bridge this gap, we establish Software-Defined Quantum Networking (SDQN) as a prerequisite for scale, prioritizing a symbiotic, dual-plane architecture where classical control dictates quantum data flow. Specifically, we synthesize reference models for SDQN and the Quantum Network Operating System (QNOS) for hardware abstraction, and adapt a Quantum Network Utility Maximization (Q-NUM) framework as a unifying mathematical lens for engineers to reason about trade-offs between entanglement routing, scheduling, and fidelity. Furthermore, we analyze Distributed Quantum AI (DQAI) over imperfect networks as a case study, illustrating how physical constraints such as probabilistic stragglers and decoherence dictate application-layer viability. Ultimately, this tutorial equips network engineers with the tools required to transition quantum networking from a bespoke physics experiment into a programmable, multi-tenant global infrastructure.

2604.01910Apr 2026

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POLARIS: PHY-Aware Spectrum Steering for Dynamic Spectrum Sharing

Dynamic Spectrum Sharing (DSS) enables flexible activation of additional spectrum resources but leaves open a key runtime question: once new spectrum becomes available, which steering mechanism should migrate connected devices toward it with minimum service disruption? We present the first PHY-aware characterization of 3GPP-compliant UE steering mechanisms, including Bandwidth Part (BWP) reconfiguration, Carrier Aggregation (CA), E-UTRA-NR Dual Connectivity (EN-DC), Connected-Mode Handover (HO), and Release and Redirection (R&R), using modem-level traces from devices connected to operational networks, collected across 1,600 executions over four months in 12 urban areas. By mapping each mechanism to observable PHY-layer milestones, we decompose steering latency into intrinsic PHY-centric execution and RRC-to-PHY completion components, revealing substantial heterogeneity: NR BWP achieves 6.25 ms mean latency with zero tail exceedance above 50 ms, while CA exceeds 1225 ms; mobility procedures remain largely modem-bound, whereas discovery-driven mechanisms experience significant RRC-to-PHY completion amplification. Guided by these measurements, we design POLARIS, an O-RAN-based system that selects the least disruptive steering mechanism via a two-parameter disruption score. POLARIS reduces mean latency by up to 85.1% and T95 by 89.7% over static or non-adaptive baselines, eliminates tail exceedance above 50 ms, and avoids high-disruption mechanisms, demonstrating that PHY-layer execution profiling enables reliable and context-aware spectrum steering in DSS-enabled networks.

2604.01087Apr 2026

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Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC

The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.

2604.02381Apr 2026

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Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) violations and post-attack recovery behavior. Our results indicate that budget-constrained adversarial jamming can induce severe and slice-dependent steady-state SLA violations. Moreover, the DRL agent's reward converges toward the clean baseline only after a non-negligible recovery period.

2604.01049Apr 2026

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Cardinality is Not Enough: Super Host Detection via Segmented Cardinality Estimation

Accurately detecting super host that establishes connections to a large number of distinct peers is significant for mitigating web attacks and ensuring high quality of web service. Existing sketch-based approaches estimate the number of distinct connections called flow cardinality according to full IP addresses, while ignoring the fact that a malicious or victim super host often communicates with hosts within the same subnet, resulting in high false positive rates and low accuracy. Though hierarchical-structure based approaches could capture flow cardinality in subnet, they inherently suffer from high memory usage. To address these limitations, we propose SegSketch, a segmented cardinality estimation approach that employs a lightweight halved-segment hashing strategy to infer common prefix lengths of IP addresses, and estimates cardinality within subnet to enhance detection accuracy under constrained memory size. Experiments driven by real-world traces demonstrate that, SegSketch improves F1-Score by up to 8.04x compared to state-of-the-art solutions, particularly under small memory budgets.

2604.02379Apr 2026

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Page 1 of 164

Networking and Internet Architecture Papers (cs.NI) - ScienceStack

3,261 papers

Analyzing Symbolic Properties for DRL Agents in Systems and Networking

2604.04914Apr 2026

View

nascTime: A Full-Stack 5G-TSN Bridge Simulation Framework with SDAP-Based QoS Mapping and IEEE 802.1AS Transparent Clock

2604.04616Apr 2026

View

Comprehensive Analysis of Cellular Uplink Performance in a Dense Stadium Deployment

2604.04371Apr 2026

View

OrbitTransit: Traffic Delivery and Diffusion for Earth Observation via Satellite Mobility

2604.04368Apr 2026

View

A Family of Open Time-Series Foundation Models for the Radio Access Network

2604.04271Apr 2026

View

RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge

2604.04243Apr 2026

View

Advanced Holographic Multi-Antenna Solutions for Global Non-Terrestrial Network Integration in IMT-2030 Systems

2604.04149Apr 2026

View

UAV Control and Communication Enabled Low-Altitude Economy: Challenges, Resilient Architecture and Co-design Strategies

2604.04044Apr 2026

View

Reimagining RAN Automation in 6G: An Agentic AI Framework with Hierarchical Online Decision Transformer

2604.03908Apr 2026

View

CCA Reimagined: An Exploratory Study of Large Language Models for Congestion Control

2604.03857Apr 2026

View

CB-VER: A Stable Foundation for Modular Control Plane Verification

2604.03539Apr 2026

View

Causal Inference for Quantifying Noisy Neighbor Effects in Multi-Tenant Cloud Environments

2604.03145Apr 2026

View

RL-Loop: Reinforcement Learning-Driven Real-Time 5G Slice Control for Connected and Autonomous Mobility Services

2604.02461Apr 2026

View

CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

2604.02284Apr 2026

View

Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction

2604.01944Apr 2026

View

Quantum Networking Fundamentals: From Physical Protocols to Network Engineering

2604.01910Apr 2026

View

POLARIS: PHY-Aware Spectrum Steering for Dynamic Spectrum Sharing

2604.01087Apr 2026

View

Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC

2604.02381Apr 2026

View

Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

2604.01049Apr 2026

View

Cardinality is Not Enough: Super Host Detection via Segmented Cardinality Estimation

2604.02379Apr 2026

View

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