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.

A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge

“…DNNs, on the other hand, can explore these deeper associations in order to identify what relational frames are relevant, and under what context, in situations where background knowledge is utilized in categorization tasks. Deep learning approaches have been used in combination with SOM in other studies to optimize task results ( Asghar et al, 2019 ).…”

“…DNNs, on the other hand, can explore these deeper associations in order to identify what relational frames are relevant, and under what context, in situations where background knowledge is utilized in categorization tasks. Deep learning approaches have been used in combination with SOM in other studies to optimize task results (Asghar et al, 2019). One implementation of deep learning which may be helpful in modeling deep abstract arbitrary features in the form of functional properties, and can extend the SOM, is from the machine learning (AI) literature, called backpropagation neural network (BPN).…”

Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

“…However, most previous applications in intrusion detection are restricted in static model analysis and lack adaptability to external environments. Although recent deep learning in neural networks produce excellent results in IDS [9], insufficient works concentrate on unknown types of attack. Immune system can well adapt to unknown attacks through its massive learning and adaptive capability [10,11].…”

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

“…However, with the deepening of research, deep learning has gained wider application and a more outstanding performance in massive data analysis, which can be used to solve intrusion detection problems of massive, high-dimensional, and nonlinear data. By constructing a nonlinear network structure with multiple hidden layers, low-dimensional features, which are easier to classify in the data, can be obtained, and the accuracy of intrusion detection is improved [28][29][30][31][32]. Hinton et al [33] proposed a deep learning method, called a deep belief network, which has attracted wide attention in academic circles.…”