Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms

Abstract

The 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.

Machine learning algorithms for inter-cell interference coordination

“…The intercell interference coordination is a problem with growing interest in LTE/LTE-A networks due to the current complexity of these networks caused by the high users' Kashaf et al, (2017) proponen un enfoque basado en Algoritmos Genéticos [Genetic Algorithms, GA], para reducir la interferencia en redes LTE y al mismo tiempo mejorar el rendimiento de la red. La solución propuesta opera en un escenario con variaciones en el tráfico, las posiciones de los usuarios y las condiciones de propagación.…”

“…Machine learning algorithms for inter-cell interference coordination, Sistemas & Telemática,16(46), 37-57. Kashaf et al, (2017) propose an approach based on GA to reduce the interference in LTE networks and, at the same time, improve the network performance. Their solution operates in an environment with variable traffic, user positions, and propagation conditions.…”