Base Station

Base stations (BSs) are the fundamental infrastructure of cellular networks, with the primary objective of providing reliable wireless communication to users. Current research focuses on optimizing BS energy efficiency, deployment strategies (including using UAVs and AI-driven siting), and resource allocation through machine learning models like neural networks, reinforcement learning, and federated learning. These advancements aim to improve network performance, reduce operational costs, and enhance the overall sustainability of next-generation cellular networks.

Papers

August 7, 2024

Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent
Yanhu Wang, Muhammad Muzammil Afzal, Zhengyang Li, Jie Zhou, Chenyuan Feng, Shuaishuai Guo, Tony Q. S. Quek
Agent Smith Style PROMPT LLM Based Agent Base Station Uncertainty Aware Deployment LLM Based Prompt

June 13, 2024

Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
Tingwei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu, Qingjiang Shi
Real World Data Power Consumption Base Station Energy Budget

January 18, 2024

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief
Federated Prompt Cooperation Multi Agent Deep Reinforcement Learning Base Station Adversarial AutoEncoder Edge Caching Next Generation Network

December 16, 2023

Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations
Jiangbin Lyu, Xu Chen, Jiefeng Zhang, Liqun Fu
Various Fast Moving Drone Base Station Motion Optimization

November 7, 2023

Emulators in JINSP
Lei Zhao, Miaomiao Zhang, Lv Zhe
Interactive Simulation Base Station Network Simulation Basic Emulator Protocol Design User Behavior Simulation

November 5, 2023

Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen Communications Optimization for Solar Small Cells
Daksh Dave, Dhruv Khut, Sahil Nawale, Pushkar Aggrawal, Disha Rastogi, Kailas Devadkar
Cellular Network Base Station Solar Cell Communication Optimization

October 5, 2023

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
Junliang Luo, Yi Tian Xu, Di Wu, Michael Jenkin, Xue Liu, Gregory Dudek
Online Optimization Quality of Service Cellular Network Base Station Approximate Dynamic Programming Network Energy Efficiency

September 4, 2023

Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning
Michail Kalntis, George Iosifidis, Fernando A. Kuipers
Online Learning Resource Allocation Online Learning Algorithm Worst Case User Throughput Open Radio Access Network Base Station Optimality Gap Adversarial Traffic

July 18, 2023

Multi-Robot Patrol Algorithm with Distributed Coordination and Consciousness of the Base Station's Situation Awareness
Kazuho Kobayashi, Seiya Ueno, Takehiro Higuchi
Multi Robot Robotic System Situational Awareness Human Consciousness Base Station

June 22, 2023

Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach
Jaehyup Seong, Mesut Toka, Wonjae Shin
Reinforcement Learning Approach Power Allocation Balancing Efficiency Base Station Energy Harvesting Rate Splitting

January 13, 2023

ML Approach for Power Consumption Prediction in Virtualized Base Stations
Merim Dzaferagic, Jose A. Ayala-Romero, Marco Ruffini
Neural Network Machine Learning Approach Power Consumption Open Radio Access Network Base Station

December 8, 2022

Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach
Nicola Piovesan, David Lopez-Perez, Antonio De Domenico, Xinli Geng, Harvey Bao
Machine Learning Approach Power Consumption Radio Access Network Base Station

November 28, 2022

Federated Learning for 5G Base Station Traffic Forecasting
Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis, Pavlos S. Efraimidis
Time Series Forecasting Traffic Prediction Mobile Network Radio Frequency Base Station

September 29, 2022

Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station
Jonas Beuchert, Marco Camurri, Maurice Fallon
Mid Range LiDAR Global Navigation Satellite System Global Localization Accurate Localization Base Station Mobile Robot Localization

September 23, 2022

Machine Learning and Analytical Power Consumption Models for 5G Base Stations
Nicola Piovesan, David Lopez-Perez, Antonio De Domenico, Xinli Geng, Harvey Bao, Merouane Debbah
Machine Learning Power Consumption Base Station Network Energy Efficiency

September 15, 2022

IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
Turgay Pamuklu, Anne Catherine Nguyen, Aisha Syed, W. Sean Kennedy, Melike Erol-Kantarci
Risk Sensitive Reinforcement Learning Smart Agriculture Base Station

September 13, 2022

Federated Meta-Learning for Traffic Steering in O-RAN
Hakan Erdol, Xiaoyang Wang, Peizheng Li, Jonathan D. Thomas, Robert Piechocki, George Oikonomou, Rui Inacio, Abdelrahim Ahmad, Keith Briggs, Shipra Kapoor
Base Station Federated Meta Learning Data Rate Radio Access Technology LTE WiFi Traffic Steering

August 25, 2022

On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
Nima Tavangaran, Mingzhe Chen, Zhaohui Yang, José Mairton B. Da Silva, H. Vincent Poor
Differential Privacy PRIvacy Leakage Differentially Private Base Station Wireless System

August 21, 2022

Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning
Michail Kalntis, George Iosifidis
Online Learning Online Learning Algorithm Sub Linear Regret Performance Guarantee Open Radio Access Network Base Station Optimality Gap Energy Efficient Scheduling

July 13, 2022

Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
Riccardo Marini, Sangwoo Park, Osvaldo Simeone, Chiara Buratti
Reinforcement Learning Meta Reinforcement Learning Vehicular Network Base Station

Base Station

Papers

Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent

Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations

Emulators in JINSP

Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen Communications Optimization for Solar Small Cells

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

Multi-Robot Patrol Algorithm with Distributed Coordination and Consciousness of the Base Station's Situation Awareness

Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach

ML Approach for Power Consumption Prediction in Virtualized Base Stations

Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach

Federated Learning for 5G Base Station Traffic Forecasting

Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station

Machine Learning and Analytical Power Consumption Models for 5G Base Stations

IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture

Federated Meta-Learning for Traffic Steering in O-RAN

On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations

Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks