Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

Abstract

Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.INDEX TERMS Artificial intelligence, genetic algorithms, ant colony optimization, self-organization networks, heterogeneous networks.

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

“…Specifically, authors in [17] classify those applications into decisionmaking tasks and classification tasks, while authors in [18] mainly concentrate on model-free strategic learning. Moreover, authors in [19] and [20] focus on the potentials of machine learning in enabling the self-organization of cellular networks with the perspectives of self-configuration, self-healing and self-optimization. To achieve high energy efficiency in wireless networks, related promising approaches based on big data are summarized in [21].…”

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

“…Application of different ML-based SON for heterogeneous networks is considered in [297], this paper also describes the unsupervised ANN, hidden Markov models and reinforcement learning techniques employed for better learning from the surroundings and adapting accordingly. PCA and clustering are the two mostly used unsupervised learning schemes utilized for parameter optimization and feature learning in SON where as reinforcement learning, fuzzy reinforcement learning, Q learning, double Q learning and deep reinforcement learning are the major schemes used for interacting with the environment [294].…”

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

“…In addition to game theory, machine learning has been considered as an effective tool in solving different network problems in 5G [15], [16]. RL is one of the most powerful tools for policy control and intelligent decision making [5], which has been widely adopted in wireless communications [17]- [19].…”

“…P ( ∇ J, ∇J > 0) denotes the probability of taking a gradient step in the right direction that increases reward. Equation (16) indicates that the probability of taking a gradient step in the right direction decreases exponentially, as the number of agents increases.…”