Intelligent SON Coordination in the 5G-and-beyond era

Stamatelatos, Gerasimos; Sgora, Aggeliki; Alonistioti, Nancy

doi:10.1109/giis56506.2022.9936918

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several promising technologies that can be implemented in order to improve QoE, including network slicing, edge computing, ML, and AI technologies. AI and ML can personalize network experiences by providing and analyzing specific data for user behaviors and preferences [ 75 , 76 ]. Network slicing could help to personalize QoE by dividing the network into multiple virtual networks, where each virtual network can be customized to meet the specific needs of the users or applications [ 77 ].…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

View full text Add to dashboard Cite

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

show abstract

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Applying Deep Reinforcement Learning for Self-organizing Network Architecture

Tu,

Ma,

et al. 2023

2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII)

View full text Add to dashboard Cite

Inefficient resource allocation and unstable connection quality for mobile devices are the primary challenges of Self-Organizing Networks (SON). Frequent handovers between base stations can result in a network burden imbalance. In contrast, unstable connection quality causes disconnection or signal interference between mobile devices and base stations, influencing network performance and reliability. In recent years, wireless communication technology has extensively utilized Reinforcement Learning (RL) to obtain the optimal strategy through continuous interaction between agents and their environments. Deep Reinforcement Learning (DRL) is based on Deep Neural Networks (DNN) architectures which allows it to handle increasingly complex network situations. This paper proposes a SON architecture based on DRL in response to the aforementioned challenges. We describe how the agent learns the optimal parameter settings through training based on various network scenarios to improve handover strategies and enhance overall network performance and resource utilization. Our proposed framework can also be applied to the present Fifth Generation (5G) network.

show abstract

Intelligent SON Coordination in the 5G-and-beyond era

Cited by 2 publications

References 27 publications

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Applying Deep Reinforcement Learning for Self-organizing Network Architecture

Contact Info

Product

Resources

About