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20 results for “LTE”

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cs.NIEmpiricalRecentJul 20, 2026

Cost-Aware Uplink MPQUIC Scheduling via Multi-Objective Bayesian Optimization

Thanh Trung Nguyen, Thanh Le, Phi Le Nguyen, Kien Nguyen

This paper proposes a Bayesian Optimization-based framework for multipath QUIC (MPQUIC) scheduling that jointly considers maximum upload completion time and total LTE usage, identifying Pareto-efficie…

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cs.NIEmpiricalRecentJul 17, 2026

App-Based Performance Characterization of Cellular and Wi-Fi Networks in Dense Stadium Deployments

Hardani Ismu Nabil, Muhammad Iqbal Rochman, S. M. Haider Ali Shuvo, Joshua Roy Palathinkal +1 more

This paper evaluates user-perceived performance and QoE of wireless networks at Notre Dame Stadium during football games using commercial smartphones for web browsing, WhatsApp messaging, and Instagra…

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cs.NIcs.PFEmpiricalRecentJul 4, 2026

Evaluating 5G-connected IoT for Power Line Temperature Prediction: Real-World Latency and Cost Trade-offs Between MEC and Cloud

Aakash Sharma, Sigmund Akselsen, Anders Andersen, Lars Ailo Bongo +1 more

This paper investigates the latency performance of Mobile Edge Computing (MEC) on a 5G cellular network for real-time power transmission line analytics, demonstrating a low latency of 44.62 ms compare…

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cs.NIPositionRecentJun 25, 2026

Toward AI-Native 6G Air Interface: A 3GPP Perspective on Protocol Framework

Xingqin Lin

This paper proposes a protocol framework for making the 6G air interface AI-native, focusing on interoperability and preserving implementation freedom.

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cs.ITeess.SPEmpiricalRecentJun 12, 2026

Generalized Framework for a Fair Comparison of Cellular and Cooperative Massive MIMO Systems

Leonard Paul Schulz, Stefan Schwarz, Gerhard Bauch

This paper introduces a graph-based framework for fair comparison of cellular, coordinated, and cell-free massive-MIMO systems, and derives compatible spectral efficiency expressions for uplink and do…

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cs.NIcs.CVEmpiricalRecentJun 30, 2026

Semantic-Aware Multiple Access via Spatial Redundancy Exploitation for Uplink-Dominant 6G Use Cases

Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb, Onur Günlü

This paper proposes a semantic-aware multiple access scheme for efficient transmission of high-volume visual data among nearby vehicles in uplink-dominant 6G systems, reducing redundant transmissions…

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eess.SPTheoreticalRecentJun 21, 2026

Outage Analysis and Fairness Design for Spatially Correlated FAS-Enabled RSMA Systems

Jinyuan Liu, Yong Liang Guan, Tuo Wu, Hong Niu +2 more

This paper derives closed-form outage probability expressions and proposes a fairness optimization algorithm for a multiuser MISO downlink system that combines rate-splitting multiple access and fluid…

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cs.ITSurveyRecentJun 10, 2026

Reconfigurable Antennas for Next-generation Mobile Communication Networks: A Comprehensive Survey and Tutorial

Yizhe Zhao, Long Zhang, Halvin Yang, Kun Yang +3 more

This paper presents a comprehensive survey on reconfigurable antennas for next-generation mobile networks, focusing on their potential and applications.

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cs.NIcs.DCEmpiricalRecentJun 29, 2026

SubEdge: A Subscriber-Centric Edge Computing Subsystem in 6G Networks for AI

Abdirazak Ali Asir Rage, Riccardo Pozza, Rahim Tafazolli

This paper proposes SubEdge, a Net4AI subsystem for per-subscriber edge computing and communication resource provisioning, ensuring service continuity during mobility.

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eess.SPEmpiricalRecentJul 27, 2026

Beam-Domain Channel Estimation for mmWave MIMO using Sub-6 GHz Out-of-Band Information

Faruk Pasic, Mariam Mussbah, Stefan Schwarz, Markus Rupp

A novel beam-domain channel estimation method for mmWave MIMO is proposed using sub-6 GHz beam-domain information for improved accuracy.

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cs.ITcs.AIRecentMay 31, 2026

Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks

Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas

The paper proposes a Digital Twin-assisted Adaptive Multi-Agent Deep Reinforcement Learning framework to intelligently manage spectrum and resources in complex, dynamic Open-RAN 6G networks utilizing…

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eess.SPcs.ITTheoreticalRecentJun 12, 2026

Repeater-Assisted Massive MIMO Downlink Performance with Calibration Errors

Kohei Ueda, Anubhab Chowdhury, Koji Ishibashi, Erik G. Larsson

This paper analyzes the effects of calibration errors on downlink beamforming in a repeater-assisted massive MIMO system and derives analytical expressions for the downlink spectral efficiency.

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eess.SPcs.AIRecentMay 29, 2026

DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks

Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli

The paper proposes DRIFT, a lightweight joint channel estimation and prediction framework, to significantly reduce pilot overhead and boost spectral efficiency in power-constrained LEO Non-Terrestrial…

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cs.NIeess.SYEmpiricalRecentJul 24, 2026

Location-Aware NAS Timer Optimization in NTN-TN Integrated Networks

Cheng Liu, Peng Hu

This paper proposes a UE-specific NAS timer optimization method for LEO NTN-TN integrated networks, reducing registration latency, energy consumption, and avoidable attempts.

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cs.NIcs.AISurveyRecentJul 17, 2026

LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization

Mazene Ameur, Abdelkader Mekrache, Bouziane Brik, Adlen Ksentini

This paper presents a tutorial-and-survey on integrating agentic AI into Next-Generation Networks (NGNs), addressing the gap in protocol integration, evaluation, and standardization alignment.

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cs.ITcs.AIcs.MAEmpiricalRecentJul 20, 2026

Autonomous Discovery of Wireless Communications Algorithms

Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Gian Marti +3 more

The paper introduces The AI Telco Engineer (AITE), a framework for autonomously designing wireless communication algorithms using large language models, achieving better performance and reduced latenc…

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cs.NIEmpiricalRecentJul 20, 2026

Self-Directed Spectrum Allocation Framework for Integrated TN-NTN 6G Networks

Vaskar Chakma, Wooyeol Choi

This paper proposes a self-adaptive channel assignment framework using Q-learning to optimize system throughput, user fairness, and interference mitigation.

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