Machine learning algorithms for inter-cell interference coordination

The current LTE and LTE-A deployments require larger efforts to achieve the radio resource manage - ment. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic op - timization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machi - ne-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.