From 4G to 5G: Self-organized network management meets machine learning

Abstract

In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

“…To this end, we define four variables to represent the state of the network. The state set variables are defined based on the constraints of the optimization problem in (3). We define the variables X 1 and X 2 as indicators of the performance of the FUE and the MUE.…”

“…In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration and maintenance of the network. In fact SONs bring two main factors in play: intelligence and autonomous adaptability [2], [3]. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs [8], [9].…”

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

“…Zhang et al [31] CNN Convolutional filters are used for feature extraction from cognitive radio waveforms for automatic recognition. Moysen et al [32] ANN Authors expressed ANN as a recommended system to learn the hierarchy of the output, which is later used in SON. Xie et al [33] RNN RNN variant LSTM is used for learning memory based hierarchy of time interval based IoT sensor data, from smart cities datasets.…”

Role of 5G Networks: Issues, Challenges and Applications

“…The last generation of cellular network, i.e. 4G network was introduce in 2011 and it is expected that 5G [3] network may standardize and deployed in 2020.…”