A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

Abstract

The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

“…Various surveys has been conducted on 5G technologies [12], [16], energy efficiency [14], [17]- [19], and on enabling technologies such CRAN [20]- [23], SDN [21], NFV [6], UDN [24], massive MIMO [25], [26], HetNets [27] and MEC [28]- [30]. The surveys on energy efficiency is either limited to the energy harvesting techniques, system design [18], virtualization [21] or architecture [19].…”

“…Author Contribution Limitations [17] This paper surveys energy efficiency issues at network level and radio access level. Energy conservation of 5G has been explored in terms of BS (architecture, switching techniques, interference reduction and deploying small cells), network (resource sharing and allocation), SDN level (energy monitoring and management) and machine learning.…”

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

“…Several energy harvesting techniques that can be utilized for wireless sensor networks are given in [79]. Here, the major techniques which can be utilized for energy harvesting are classified as cooperative communication [41,80,81], energy harvesting and wireless charging [76,82,83], radio optimization [84], modulation optimization [46,85,86], sleep/wake up mechanisms [87][88][89], multi path routing and data reduction techniques such as adaptive sampling, compression and network coding.…”

“…Web caching is a technique where the content which is reused repeatedly by the end users is stored at intermediate nodes in the network rather than at the original remote server. This technique enabled to reduce retransmissions of data, enhance the performance metrics of the network and also provide a high quality of service (QoS) to the users [87,129]. Further, an illustration of a mobile cellular network with and without caching and its effect on data congestion in the network is shown in Figure 8.…”

“…Web caching led to content delivery networks (CDNs) that are capable of replicating content across the internet and reducing more than 50% of the data traffic that is directed to the remote main servers which helped in reducing the amount of backhaul traffic in the network [129]. Further, enabling caching in wireless networks is another means of achieving energy efficiency in the emerging networks [87,[129][130][131][132][133][134][135]. The power consumption for data transfer from a remote main server to the end user is higher than the data transfer from an intermediate cache node to the end user.…”