95 papers
This paper introduces CovertComBench, a specialized benchmark for evaluating Large Language Models in wireless covert communication, revealing that while current models excel at conceptual understanding and code generation, they significantly struggle with the rigorous mathematical derivations required for security-constrained optimization.
This paper presents a comprehensive overview of 5G network slicing as a key architectural enabler for efficient resource utilization, focusing specifically on its business aspects through profit modeling for both own-slice implementation and resource leasing, while also addressing existing challenges and future research directions.
This paper addresses the problem of role classification in enterprise networks by introducing two practical algorithms that group hosts based on evolving connection patterns to simplify network management and enhance monitoring accuracy, demonstrating their effectiveness through commercial implementation and significant reduction in host grouping complexity.
This paper demonstrates through game-theoretic analysis and simulation that the non-cooperative behavior of nodes under the 802.11 DCF and 802.11e MAC protocols leads to inefficient Nash equilibria, whereas decoupling channel allocation from individual transmission strategies can achieve superior throughput for all competing nodes.
This paper introduces PixelConfig, a framework for reverse-engineering Meta Pixel configurations, which reveals that default settings drive widespread adoption of activity and identity tracking features capable of capturing sensitive health data, while existing tracking restriction mechanisms offer limited practical protection.
This paper introduces the NP-complete Mobile Base Station Optimal Tour (MOT) problem for efficient data collection in wide-area IoT sensor networks and proposes a polynomial-time greedy heuristic that outperforms state-of-the-art approaches by balancing travel cost, coverage gain, and execution time while adhering to energy and restricted-area constraints.
This paper proposes a column-generation-based algorithmic framework to efficiently solve both splittable and unsplittable variants of the capacitated Multi-Commodity Flow problem with convex, potentially non-differentiable, link cost functions, offering a robust optimization approach for managing traffic in telecommunication networks.
This paper introduces ZeroSiam, an efficient asymmetric Siamese architecture that prevents model collapse during test-time entropy minimization by employing asymmetric divergence alignment, thereby enhancing adaptation and reasoning performance across diverse vision and language tasks with negligible overhead.
This paper proposes a hierarchical reinforcement learning framework that jointly optimizes antenna tilt angles and the data collection ratio between a physical network and its digital twin to maximize user data rates while minimizing communication overhead and delay.
This paper demonstrates that channel-centric models, including ray-tracing simulators, fail to accurately predict end-to-end throughput in private 5G networks due to systematic over-estimation of MIMO spatial layers, whereas data-driven Gaussian process models trained on direct measurements provide significantly more reliable predictions for communication-aware robot planning.
This paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing framework that utilizes a Proximal Policy Optimization (PPO) and Linear Programming (LP) hybrid scheme to jointly optimize offloading ratios, semantic symbols, and RIS phase shifts, achieving a 40–50% reduction in end-to-end latency compared to existing methods.
This paper proposes the Online-Within-Online Fair Multi-Task Learning (OWO-FMTL) framework, which leverages a dual-loop mechanism with primal-dual updates to ensure long-term equitable inference performance for heterogeneous users in AI-RANs while maintaining low computational overhead.
This paper presents a data-driven methodology using geospatial analytics and machine learning to accurately estimate and identify the drivers of spectrum demand variations across urban regions, achieving 70% predictive accuracy to support flexible spectrum access policies for future 6G networks.
This paper introduces HR-GAT, a hierarchical resolution graph attention network that leverages geospatial data to predict spectrum demand with 21% higher accuracy than baseline models, effectively addressing spatial autocorrelation challenges to enable more efficient spectrum sharing and policy-making.
This paper introduces DEBT control, a joint routing and flow-control protocol for payment channel networks that uses price-based mechanisms and gradient descent optimization to guide the network toward an optimal steady-state operating condition while providing convergence guarantees.
This paper proposes a joint user association and resource allocation framework for Adaptive Semantic Communication in 5G and beyond networks, which dynamically adjusts DNN-based transceivers to address user heterogeneity and maximizes system utility under energy and latency constraints through a decomposed, polynomial-time algorithm.
This paper presents an analytical MAC-layer model for 5G NR Sidelink Mode 2 that explicitly incorporates standardized Semi-Persistent Scheduling features to derive closed-form expressions for packet reception ratio, which are validated against ns-3 simulations to provide design insights for enhancing 6G sidelink reliability.
This paper introduces Silicone Ethernet (SEth), a wireless, battery-free system that embeds sensing, communication, and power transfer capabilities directly into a conductive silicone substrate to overcome the cabling limitations of fine-grained robotic touch sensing.
This paper presents a comprehensive comparative study of recent advances in Internet of Things (IoT) intrusion detection systems, analyzing their architectures, classifications, and evaluation methodologies to address critical security challenges.
This paper proposes a topological framework utilizing Hodge decomposition to analyze serverless service flows, distinguishing between locally correctable errors and globally persistent harmonic inefficiencies to derive actionable remediation strategies without requiring complete architectural restructuring.