Emerging of Machine Learning and Deep Learning Technology: Addressing in Intelligent Wireless Network Optimization

Abstract

Wireless networks have grown into an essential component of modern life as a result of the proliferation of wireless technology over what has been a decade. The science underlying machine learning (ML) focuses on encouraging computers to act rather than additional programming. Throughout the prior ten years, machine learning has been helping us develop autonomous transportation systems, usable speech detection, effective web searches, and a much greater awareness of the genetic code of people. Deep learning (DL) is a breakthrough technology that makes automatically and self-sufficient managing networks possible. Incorporating DL creativity into wireless networks has the possibility of helping improve the efficiency of systems in instantaneously as well as replace the manually performed strategies currently required in engineers typically do the following-intensive network management obligations. This paper presents an essential understanding of during which exactly the superiority of ML based approach originates from compared to the traditional modeling based strategies by carefully reviewing recent attempts in employing DL for addressing wireless network optimization challenges. Along with discovering comparisons and difficulties, the fundamental research difficulties including a few prospective research areas for completely realizing the possibility of ML in wireless network optimization have also been highlighted. At last, learning-related comparisons between machine learning as well as deep learning are made.