The Role Of Energy Efficiency In Las Vegas’s Clean Energy Transition – On the performance of a non-orthogonal multiple energy harvesting relay system with imperfect channel state information over fading Rayleigh channels

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The Role Of Energy Efficiency In Las Vegas’s Clean Energy Transition

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Entry: May 13, 2019 / Revised: July 9, 2019 / Accepted: July 11, 2019 / Published: July 15, 2019

The rapid focus of various verticals on the upcoming 5th generation (5G) networks expects the network to support higher data rates and improve service quality. This demand has so far been met by the use of complex transmission methods, including massive multiple input multiple output (MIMO), millimeter wave (mmWave), as well as by bringing computing power closer to users through advanced processing units in base stations. The future evolution of networks also opens up new business horizons for operators, and the need for not only resource-efficient but also an energy-efficient ecosystem is strongly felt. Small cell deployment is envisioned as a promising answer to handle heterogeneous traffic, but the negative economic and environmental impacts should not be overlooked. Considering that 10% of the world’s energy consumption is due to the information and communication technology (ICT) industry, energy efficiency has become one of the key performance indicators (KPI). Various optimization, game theory, and machine learning approaches have been investigated to improve power allocation for downstream and upstream channels, as well as other energy efficient/saving approaches. This paper reviews recent work addressing the energy efficiency of radio access as well as the basics of wireless networks, highlighting challenges and open issues.

Advances in telecommunications systems around the world are constantly pushing wireless infrastructure to be more robust and scalable. The ever-increasing demand for higher data rates and higher quality of service has been a strong constraint when energy savings have to be considered. With the advent of 5G, data speeds up to 1 Gbps were expected. In addition, with the emergence of a large number of heterogeneous devices, including sensors for home security, tablets, and wearable health monitors, the computing power of base stations should increase. It has been predicted that a 50% increase in the computing capacity of base units will be needed to overcome this traffic explosion [1]. Thus, the focus on energy efficiency should include optimization of computational complexity in addition to optimization of transmission power.

About 75% of the information and communication technology (ICT) industry is expected to be wireless by 2020, and today 5% of the world’s carbon footprint comes from this industry alone. Consensus between academia and industry dictates that the expected 1000× capacity increase should be achieved with current energy consumption or less [2]. Thanks to worldwide energy saving efforts, energy consumption in the 5G industry in terms of bits/joules is considered as an important design parameter. The 4th generation (4G) introduced the concept of small cells to increase coverage and capacity. Therefore, [3] performed an analysis of energy consumption per unit area for heterogeneous cell placement for fourth generation networks. With 5G, small cells are inevitable in deployment because of the advantage of improved traffic handling in a smaller area, as well as the shorter cell range that comes from using higher frequencies. However, an increase in the number of base stations translates into higher energy consumption, although the increase in consumption will not be linear. Small cells, or in other words, density, requires complex resource management. Recently, intelligent resource allocation and management methods using machine learning algorithms have been proposed to help next-generation radios in their autonomous configuration to improve data rates, energy efficiency, and reduce interference. In general, the emerging sophistication has increased the energy consumption in both the User Equipment (UE) and the network side, and therefore objective functions have been developed to maximize energy efficiency, stored energy, and energy transfer [4] . Many existing methods to improve energy efficiency include using green energy sources for base stations, changing the coverage area of ​​the base station depending on the load level, putting the lightly loaded base stations to sleep, and balancing the load by handing over UEs. macro base. A survey of these technologies for the 5G Radio Access Network (RAN) can be found in [5].

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This survey aims to contribute to a greener and more sustainable telecommunications ecosystem by reviewing and integrating some of the latest energy-saving ideas and techniques at the base station and network level. A high-level diagram shows the regions shown in Figure 1. Some notable examples include the introduction of the newer Radio Resource Management (RRC) mode for context signaling and reducing redundant mode changes [6]. The use of advanced clustering and caching techniques on the RAN side has been appreciated for their benefits in improving the latency of receiving requested data by a group of users and possibly eliminating the network congestion factor caused by a large number of requests for the same. content [7, 8]. A case study of sharing commercial resources between different operators gives beneficial results in terms of reduced deployment costs and good data rates with minimal interference between them [9]. The following sections introduce the basics of energy efficiency, justify the need to calculate energy consumption, and then present recent research work on optimization at different levels of architecture. This survey is unique in its comprehensive approach to energy efficiency by covering radio, core and 5G computing. This article also differs from literature surveys [1, 2, 3, 4] because it focuses on works published in the last few years, where the majority of studies focus on specific concepts of the new 5G standard.

A formal relationship between energy efficiency and signal to interference noise ratio (SINR) is presented in [2] using the concept of bit/joule. Meanwhile, reference [4] provides a basis for energy efficiency in different parts of the network, including the base stations and the core network. In the literature, energy conservation and the use of green energy resources have been the two main approaches to propose energy efficiency. Among the methods of energy saving, the methods of switching off cells are widely used. For example, in the EU FP7 project ABSOLUTE, an energy broker is proposed, which uses a minimum capacity for the activation of base stations [10]. In several other studies, data loading has been considered as an energy efficient approach. Moreover, the authors in [11] collected several methods to not only reduce energy consumption from traditional energy sources, but also to explore new energy efficiency (EE) schemes in the End-to-End (E2E) system. One of the interesting notes of the authors includes the implementation of the 3rd Generation Partnership Project (3GPP) EE Manager, which will be responsible for monitoring the energy demand in the E2E session and implementing the necessary policies to meet the current energy demand.

In addition to energy saving approaches, simultaneous wireless energy transfer has recently been studied. Furthermore, local caching techniques are useful for easing the load on the backhaul network by storing content locally and limiting retransmissions, thus reducing energy consumption. Similarly, cloud-based RAN is envisioned as a possible solution for distributed computing in [2, 4, 12]. Many of the tasks previously performed by a base station (BS) have been moved to the data center and only decision making for radio frequency (RF) circuits.

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