Reinforcement Learning Applications in Self-Organizing Networks
Author(s): Varinder Kumar Sharma
Publication #: 2508024
Date of Publication: 05.01.2021
Country: United States
Pages: 1-8
Published In: Volume 7 Issue 1 January-2021
DOI: https://doi.org/10.5281/zenodo.17062920
Abstract
The rapid expansion of mobile communication networks and the increasing demand for high-performance wireless services have propelled the evolution toward more intelligent and autonomous network management solutions. Self-Organizing Networks (SONs) represent a key paradigm shift aimed at automating configuration, optimization, and healing processes in next-generation wireless systems such as LTE and early 5G deployments. Traditional SON implementations, however, often rely on static rule-based mechanisms that lack adaptability in dynamic network environments. Reinforcement Learning (RL), a subdomain of machine learning inspired by behavioral psychology, offers a promising alternative through its capacity to learn optimal decision-making strategies via interaction with the environment. This paper explores the integration of reinforcement learning techniques into SON functions to address challenges such as dynamic resource allocation, handover optimization, interference mitigation, and fault management.
Keywords: Reinforcement Learning, Self-Organizing Networks (SON), LTE, 5G, Handover Optimization, Network Automation, Q-learning, Policy Gradient, Interference Management, Radio Resource Management, Fault Detection, AI in Telecom, Adaptive Networking, Machine Learning, Markov Decision Process (MDP).
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