Abstract
As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.
Highlights
Until the 4th generation of mobile communication standard, the focus was to deliver high data rate
This paper aims to provide a comprehensive survey of recent advances in energy efficiency techniques at access, edge, and core network utilizing machine learning
NOVELTY AND CONTRIBUTION This paper aims to provide a comprehensive survey on addressing energy efficiency in 5G network challenges encountered in enabling technologies such as mmWave, Cloud Radio Access Network (CRAN), massive MIMO, Network Function Virtualization (NFV), hetNets, small cells, and Software-Defined Network (SDN) using machine learning
Summary
Until the 4th generation of mobile communication standard, the focus was to deliver high data rate. Mughees et al.: Towards Energy Efficient 5G Networks Using Machine Learning reliability) and low latency These applications usually do not require a high data rate. Such requirements have resulted in the need to design a communication infrastructure that can readily adapt to changes In this regard, 5G networks are being designed to provide pervasive networking, high data rates, coverage, reliability, and low latency. 5G networks are being designed to provide pervasive networking, high data rates, coverage, reliability, and low latency Meeting such diverse requirements has resulted in increased ICT energy consumption. C. NOVELTY AND CONTRIBUTION This paper aims to provide a comprehensive survey on addressing energy efficiency in 5G network challenges encountered in enabling technologies such as mmWave, CRAN, massive MIMO, NFV, hetNets, small cells, and SDN using machine learning.
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