A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions
Abstract The fifth-generation (5G) network is considered a game-changing technology that
promises advanced connectivity for businesses and growth opportunities. To gain a …
promises advanced connectivity for businesses and growth opportunities. To gain a …
ORAN-B5G: A next generation open radio access network architecture with machine learning for beyond 5G in industrial 5.0
Autonomous decision-making is considered an intercommunication use case that needs to
be addressed when integrating open radio access networks with mobile-based 5G …
be addressed when integrating open radio access networks with mobile-based 5G …
6G: Vision, Applications, and Challenges
Global initiative and research on 6G have grown rapidly since 2018. The rollout of 5G is
driving our life, industry, and society toward a connected and smart world. 6G is envisioned …
driving our life, industry, and society toward a connected and smart world. 6G is envisioned …
The cost of training machine learning models over distributed data sources
Federated learning is one of the most appealing alternatives to the standard centralized
learning paradigm, allowing a heterogeneous set of devices to train a machine learning …
learning paradigm, allowing a heterogeneous set of devices to train a machine learning …
Towards energy-aware federated traffic prediction for cellular networks
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G)
networks and beyond, as accurate forecasting is essential for intelligent network design …
networks and beyond, as accurate forecasting is essential for intelligent network design …
A general approach for traffic classification in wireless networks using deep learning
Traffic Classification (TC) systems allow inferring the application that is generating the traffic
being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have …
being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have …
Carbon-aware machine learning: A case study on cellular traffic forecasting with spiking neural networks
Cellular traffic forecasting is an essential task that enables network operators to perform
resource allocation and anomaly mitigation in fast-paced modern environments. However …
resource allocation and anomaly mitigation in fast-paced modern environments. However …
Multi-task learning for efficient management of beyond 5G radio access network architectures
Automation of Radio Access Network (RAN) operation is a fundamental feature to manage
sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of …
sustainable and efficient Beyond Fifth-generation wireless (5G) networks, in the context of …
The implications of decentralization in blockchained federated learning: Evaluating the impact of model staleness and inconsistencies
Blockchain promises to enhance distributed machine learning (ML) approaches such as
federated learning (FL) by providing further decentralization, security, immutability, and trust …
federated learning (FL) by providing further decentralization, security, immutability, and trust …
Resource-Efficient Spectrum-Based Traffic Classification On Constrained Devices
Traffic Classification (TC) systems are designed to identify the applications generating
network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques …
network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques …