Adaptive Adjustment of Enhanced Inter-Cell Interference Coordination using Machine Learning in LTE Heterogeneous Networks

Abstract

To satisfy data-communication demands of mobile communications through the deployment of small cells can maximize spectrum efficiency Therefore the heterogeneous networks (HetNets) have been widely discussed in LTE system However because of the serious mutual interference between cells it will increase the probability of service interruption Therefore 3GPP LTE standard Release 10 formulate enhanced inter-cell interference coordination (eICIC) technique to improve this problem eICIC include two technology almost blank subframe (ABS) and cell range expansion (CRE) This thesis adopt fuzzy q-learning approach and regard distributed process as main idea We consider call bock rate (CBR) and call drop rate (CDR) as key performance indicators to discuss adaptive adjustment of cell individual offset (CIO) and almost blank subframe ratio (ABS Ratio)

Date of Award	2020
Original language	English
Supervisor	Szu-Lin Su (Supervisor)