Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 57

User association and load balancing is a challenge that has been attracting researchers of wireless networks. The question is how to optimally assign users to base stations and distribute the load in a balanced way among network base stations. The aim is to achieve high QoS to all users and at the same time efficiently utilize network resources.

The authors of [22] investigated the use of deep learning to perform user-cell association to maximize the total data rate in massive multiple input multiple output (MIMO) networks. The authors show how a deep neural network; that gets the geographical positions of users as input; can be trained to approach optimal association rule with low computational complexity. Association rule is updated in real-time considering mobility pattern of network users.

A method for cell outage detection was proposed in [23] using neural networks and unsupervised learning. The main feature of the method is the training of the network which can be performed in advance even when the cell outage data is not available. Moreover, the developed method could work in time-varying wireless environments. The machine learns from measurement reports of signal power which are collected by mobile devices.

The research work in [24] proposes a distributed, user-centric ML-based association scheme. The algorithm is based on fuzzy Q-learning, where each cell tries to maximize its throughput under infrastructure capacity and QoE constraints. With this scheme, cells broadcast data values to guide users to associate with best cells. The values reflect the possibility of a cell to satisfy a throughput performance level. Each cell tries to learn the optimal values through iterative interaction with the environment. In [25], the authors used realistic mobile network data and investigated methods for failure prediction. They compared the performance of the SVM and several neural networks.