# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
###### Abstract  
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks. deep learning (DL); self-organizing networks (SON); 5G; autoencoder (AE); mobility load balancing (MLB); cell outage detection; intrusion detection +
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networks. Many state-of-the-art SON solutions are available on the market including AirHop's eSON from Jio and AirHop communications [2], which received the 2016 Small Cell Forum Heterogeneous Network Management Software and Service reward [3]. However, the SON solutions available in the market are based on the following:  
* Automated information processing limited to low complexity solutions like triggering and heuristics;
* Many operations are done manually; large network data is not utilized;
* Coordination of SON functions is not done yet;
* Self optimization is not extended beyond the Radio Access Networks (RANs) segment. Therefore, it is a requirement of a cognitive and an intuitive network management not only to configure, manage, and optimize the network but also to teach the network data to behave and interact with the changing situations and requirements within the network in the future. In this paper, the use of the deep learning method is assessed for application in 5G self organizing networks (SON). Many processes involved in the management and operation of cellular networks require automation to reduce or avoid human involvement, which is often time consuming and tedious. Cell outage detection, intrusion detection, and mobility load balancing are strenuous tasks to manage and configure properly. A recent network outage incident in the UK cost Ericsson 100 million pounds in damage [4]. The use of an AI-powered intelligent SON could have saved Ericsson these costs. A few decades ago, machine learning (ML) was a less important field, but with the progress of computation power and graphical processing units (GPUs) and the increasing availability of valuable data availability, the field of ML has advanced by leaps and bounds. All of these factors have driven modern research to unleash the value of the data by applying state-of-the-art ML techniques. Common machine learning techniques including supervised or unsupervised methods work traditionally, and their results are hindered by various pitfalls including manual feature extraction, slow processing, and computation. Therefore, deep learning has emerged as an alternate ML technique to solve notoriously hard natural language processing, computer vision, and object detection and recognition problems. Deep learning aims to produce machines with full capability to interact with the environment, even if unexpected and strange scenarios arise.