Original scientific paper
https://doi.org/10.7305/automatika.2014.12.502
Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters
Moazzam Islam Tiwana
; Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, Pakistan
Abstract
With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput.
Keywords
Handover Margin; LTE; Reinforcement Learning; Fuzzy Q-Learning; SON
Hrčak ID:
133206
URI
Publication date:
12.1.2015.
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