Abstract
ABSTRACTThe design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.
Highlights
The 4G and beyond mobile access technology promises to deliver high data rates from 100 Mbps to 1Gbps along with a high quality-of-service (QoS) provision for a variety of applications [1,2]
This work focuses on self-optimization where performance measurements from the network known as key performance indicators (KPIs) are used to optimize the radio resource management (RRM) parameters
fuzzy Q-learning (FQL) has been used for the optimization of mobility parameters of both GSM Edge Radio Access Networks (GERAN) [14] and Long Term Evolution (LTE) network [15]
Summary
The 4G and beyond mobile access technology promises to deliver high data rates from 100 Mbps to 1Gbps along with a high quality-of-service (QoS) provision for a variety of applications [1,2]. This work focuses on self-optimization where performance measurements from the network known as key performance indicators (KPIs) are used to optimize the radio resource management (RRM) parameters. This leads to an optimal network performance. With the advent of Long Term Evolution (LTE), the research on LTE self-optimization involves RRM parameters like interference mitigation using inter-cell interference coordination (ICIC) [9,10], load balancing [11,12] and bandwidth allocation [13]. FQL has been used for the optimization of mobility parameters of both GSM Edge Radio Access Networks (GERAN) [14] and LTE network [15].
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