2 research outputs found
Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)
Machine learning (ML) is included in Self-organizing Networks (SONs) that are
key drivers for enhancing the Operations, Administration, and Maintenance (OAM)
activities. It is included in the 5G Standalone (SA) system is one of the 5G
communication tracks that transforms 4G networking to next-generation
technology that is based on mobile applications. The research's main aim is to
an overview of machine learning (ML) in 5G standalone core networks. 5G
Standalone is considered a key enabler by the service providers as it improves
the efficacy of the throughput that edges the network. It also assists in
advancing new cellular use cases like ultra-reliable low latency communications
(URLLC) that supports combinations of frequencies.Comment: 5G, Machine learning (ML), Self-organizing Networks (SONs), 5G
Standalone, Artificial Intelligence (AI
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed
through smart devices connected to the Internet. In the wake of disruptive IoT
with a huge amount and variety of data, Machine Learning (ML) and Deep Learning
(DL) mechanisms will play a pivotal role to bring intelligence to the IoT
networks. Among other aspects, ML and DL can play an essential role in
addressing the challenges of resource management in large-scale IoT networks.
In this article, we conduct a systematic and in-depth survey of the ML- and
DL-based resource management mechanisms in cellular wireless and IoT networks.
We start with the challenges of resource management in cellular IoT and
low-power IoT networks, review the traditional resource management mechanisms
for IoT networks, and motivate the use of ML and DL techniques for resource
management in these networks. Then, we provide a comprehensive survey of the
existing ML- and DL-based resource allocation techniques in wireless IoT
networks and also techniques specifically designed for HetNets, MIMO and D2D
communications, and NOMA networks. To this end, we also identify the future
research directions in using ML and DL for resource allocation and management
in IoT networks.Comment: 21 pages, 3 figure