Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

3Deep Reinforcement Learning for Wireless Network

Bharti Sharma¹*, R.K Saini¹, Akansha Singh² and Krishna Kant Singh³

¹ Department of Computer Application, DIT University, Dehradun, Uttarakhand, India

² Department of Computer Science Engineering, ASET, Amity University Uttar Pradesh, Noida, India

³ Department of ECE, KIET Group of Institutions, Ghaziabad, India

Abstract

The rapid introduction of mobile devices and the growing popularity of mobile applications and services create unprecedented infrastructure requirements for mobile and wireless networks. Future 5G systems are evolving to support growing mobile traffic, real-time accurate analytics, and flexible network resource management to maximize user experience. These tasks are challenging as mobile environments become increasingly complex, heterogeneous and evolving. One possible solution is to use advanced machine learning techniques to help cope with the growth of data and algorithm-based applications. The recent success of deep learning supports new and powerful tools that solve problems in this domain. In this chapter, we focus on how deep reinforcement learning should be integrated into the architecture of future wireless communication networks is presented.

Keywords: Big data, cellular network, deep learning, machine learning, neural network, reinforcement learning, wireless network, IoT

3.1 Introduction

Wireless networking landscape is undergoing a major revolution. The smart phone-oriented networks of the past ...