scholarly journals Enhancing User Fairness in OFDMA Radio Access Networks Through Machine Learning

2019 ◽  
Author(s):  
Ioan-Sorin Comsa ◽  
Sijing Zhang ◽  
Mehmet Aydin ◽  
Pierre Kuonen ◽  
Ramona Trestian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Access Networks ◽  
Radio Access Networks ◽  
Radio Access

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

2015 ◽  
Vol 87 (3) ◽  
pp. 815-835 ◽  
Author(s):  
Pavel Baltiiski ◽  
Ilia Iliev ◽  
Boian Kehaiov ◽  
Vladimir Poulkov ◽  
Todor Cooklev
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Access Networks ◽  
Spectrum Monitoring ◽  
Radio Access Networks ◽  
Radio Access

Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

2020 ◽  
Vol 7 (10) ◽  
pp. 9413-9425
Author(s):  
Shi Yan ◽  
Minghan Jiao ◽  
Yangcheng Zhou ◽  
Mugen Peng ◽  
Mahmoud Daneshmand
Keyword(s):  
Machine Learning ◽  
Access Networks ◽  
User Association ◽  
Learning Approach ◽  
Radio Access Networks ◽  
Radio Access ◽  
Machine Learning Approach ◽  
Content Placement

scholarly journals On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 689 ◽  
Author(s):  
Vahid Kouhdaragh ◽  
Francesco Verde ◽  
Giacinto Gelli ◽  
Jamshid Abouei
Keyword(s):  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Learning Strategies ◽  
Cost Effective ◽  
Access Networks ◽  
High Complexity ◽  
Severe Limitation ◽  
Radio Access Networks ◽  
Aerial Vehicles ◽  
Radio Access

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

Energy-Efficient Full-Duplex Enabled Cloud Radio Access Networks

2020 ◽  
Vol E103.B (1) ◽  
pp. 71-78
Author(s):  
Tung Thanh VU ◽  
Duy Trong NGO ◽  
Minh N. DAO ◽  
Quang-Thang DUONG ◽  
Minoru OKADA ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Access Networks ◽  
Full Duplex ◽  
Cloud Radio Access Networks ◽  
Radio Access Networks ◽  
Radio Access

On the Design of Computation Offloading in Fog Radio Access Networks

2019 ◽  
Vol 68 (7) ◽  
pp. 7136-7149 ◽  
Author(s):  
Zhongyuan Zhao ◽  
Shuqing Bu ◽  
Tiezhu Zhao ◽  
Zhenping Yin ◽  
Mugen Peng ◽  
...  
Keyword(s):  
Access Networks ◽  
Computation Offloading ◽  
Radio Access Networks ◽  
Radio Access

scholarly journals Energy-Efficient Orchestration of Metro-Scale 5G Radio Access Networks

2021 ◽  
Author(s):  
Rajkarn Singh ◽  
Cengis Hasan ◽  
Xenofon Foukas ◽  
Marco Fiore ◽  
Mahesh K. Marina ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Access Networks ◽  
Radio Access Networks ◽  
Radio Access
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