Abstract
As the Information and Communication Technology (ICT) industry makes rapid progress towards integrated mobile computing, there is a need to consider the impact of the envisaged 5G network system on the carbon footprint. 5G systems are expected to consume enormous amounts of energy to operate compared to the current LTE networks. Fortunately, there is a range of technologies that can be applied either consecutively or concurrently. This paper reviews the following possible solutions: Lean carrier design, dynamic beamforming, dynamic energy saving for small cells, densification with very small cells, advanced network sharing and network virtualization.
Keywords: Mobile computing, 5G technologies, Energy Efficiency, MIMO
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Introduction
The era of the fifth generation (5G) networks has dawned. This means that various pertinent issues related to capacity, security, heterogeneity of improvements, end-to-end-delay, throughput, and scalability must be addressed in order to achieve a unified 5G infrastructure and efficient communications system [5] [6] [7]. As the electronics and communication industry spearheads the world towards this goal, it is important to consider how 5G technologies will play a part in the environmental sustainability of future societies, as is happening with other initiatives and frameworks like smart grids and smart cities [8]. Energy efficiency is no doubt a central issue in the transformation of mobile computing networks.
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One major goal of attaining energy efficiency, with the exception of ecological value, is the reduction of costs in the operation of mobile networks in addition to increasing customer satisfaction with improved battery life. At the moment, it is not clear how 5G networks will contribute to power efficiency in the mobile computing arena as most agencies are only focused on amplifying bandwidths as an overlay on top of current networks. Energy efficiency should be considered as an integral part wireless systems.
In an effort to unravel the issue of energy consumption in the context of 5G networks and mobile computing, this paper attempts to review technologies and setups that can serve as potential solutions. It also reviews a growing body of research on energy savings in 5G while focusing on recent advances in technologies that could bring energy efficiency in 5G and mobile computing domains.
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Problem Statement
The 5G concept adopts more sophisticated networks, denser deployment, and an almost anytime-anywhere goal. These complex features pose six main challenges: higher data rates, higher capacity, lower end-to-end latency, massive device connectivity, reduced cost, and quality of experience provisioning.
Within the 5G concept, wireless and mobile traffic volume will skyrocket by a thousand-fold in the next ten years due to the 50 billion devices presumed to be connected to the cloud by the year 2020 based on the anytime-anywhere goal [12]. Such a large number of devices will present challenges which should be countered by enhancing capacity, spectrum utilization, cost efficiency, and, most importantly, energy efficiency [12]. It is important to note that the 5G cellular framework is heterogeneous in nature as it blends microcells, microcells, small cells, and relays in its architecture. The reason behind this is the interconnectivity capabilities for emerging technologies such as Massive MIMO network, mobile and static small-cell networks, and cognitive Radio network. It also explains the incorporation of function virtualization (NFV) cloud, small cell access points, Device to Device (DVD) communication, and the Internet of things (IOT) in the 5G concept. The mobile small cell concept is an important part of the 5G cellular system which consists of small cell concepts and mobile relay.
The expected exponential growth in data traffic volume, the number of connected devices, and diverse requirements means that power consumption is a central issue. 5G networks will have much demanding power requirement than their LTE and HSPA counterparts and battery operations are anticipated to be short-lived and much more expensive. In the 5G architecture, the Active Antenna Unit (AAU) [13] has now replaced the Remote Radio unit (RRU) since it is a MIMO with multiple integrated transmitters and antennas that are capable of steering the RF beam at all users on real time. This calls for the installation of many transmitters which are under the control of a BaseBand Unit (BBU) that calculates a mathematical beam to create a solution for every user relative to their movement while processing gigabyte of internet traffic at the same time. This requires massive processing power which means the BBU will consume energy akin to a space heater. Additionally, many transmitters translate to more power requirements. Energy efficiency is, thus, the key to achieving the 5G network goal.
Methodology
The research process commenced with a question: What solutions are viable in reducing power consumption in 5G networks? The researcher used grounded theory to methodically gather and analyze data in the topic of interest. As empirical data were reviewed, repeated ideas, elements and concepts became apparent. These represented unique codes in the data. The codes were then grouped into main concepts and categories and later used as the basis for the discussion section. The researcher’s choice of grounded theory was backed by the methods ability to improve a method before application. As the realization of 5G networks instigates, a new theory in the subject of energy efficiency can be grounded on current possible solutions in order to rate the effectiveness of each solution scientifically.
Discussion
Lean Carrier Design
A lean carrier design is a radio frame with limited signals that are not always active like cell-specific signals [11]. This approach minimizes overall transmission by cutting back on transmissions that do not contribute to direct user data transfer. In non-lean designs, not all signals that are sent and received through a network are directly related to data transmission. Therefore, 5G networks can reduce non-data transmissions such as synchronization signals and control and system signals to save energy. With the use of DTX technology, a Base Station (BS) can enter sleep mode when there are no transmissions to reduce idle time energy consumption [10]. The operation behind this model relies on disabling some unneeded power-hungry components. Because the cell is not completely switched off in DTX technology, the sleeping capacity of a system is associated with its empty transmission duration, which is normally denoted as DTX duration.
Dynamic Beamforming
Dynamic beamforming refers to the application of MIMO and advanced beamforming techniques to enable the utilization of transit energy in a specific receiver or for localized transmission, which sequentially improves energy efficiency by reducing the amount of energy dissipated in an area with few or no users. In a typical Massive MIMO (MM) configuration, a BS equipped with many antennas (M >> K) serves single-antenna equipment (UEs) [9].
The deployment of many antennas in at the BS produces an interesting phenomena known as a favorable configuration in which the channel becomes almost deterministic because the radio links between the BS and the UE are nearly orthogonal relative to each other. This results from the asymptotic disappearance of uncorrelated noise and intra-cell interference. Thus, an MM system can achieve massive energy efficiency gains due to favorable configuration since multiple orders of array and multiplexing can also be achieved. Specifically, mmWave MIMO systems can benefit immensely from hybrid beamforming which reduces uplink and downlink transmit power via coherent combination as well as increased apertures in antennas. In such a network, hybrid transceivers combine lower-dimensional digital signal processing units with power amplifiers and analog phase shifters.
Dynamic Energy Saving for Small Cells
Dynamic energy saving schemes employ the concept of transmission based on needs where network nodes transmit only to users who need the transmissions [4]. This enables energy saving in occasions where no users are available as well as when no needs exist in the system. Real-time measurements have demonstrated that most cells are usually ‘empty’ when it comes to needs for a significant part of the way. Hence, it is possible to achieve high energy efficiency with this technique. A case in point is a system with two levels of sleep modes wherein the first is deep sleep with no possibility of synchronization while the second is light sleep with a possibility of cell discovery and access. In the latter, the cell can transmit necessary signals. When the cell is not in sleep mode, it will enter operation mode, enabling normal transmission of data to users. In addition, in this Composable Control function (CP)/ Programmable control function (UP) split may be used where control plane connectivity is provided by macro sires while small cells function as pure UP modes – data/capacity boosters that can switch on or off depending on needs.
Densification with very Small Cells
Densification involves bringing many nodes to the network and reducing the distance and ISD to the user by deploying them below rooftop heights [3]. Densification reduces path-loss and, consequently, the power needed to reach a user over long distances. This reduces energy consumption on the side of the UE as well as at the network node.
Network sharing and Network Virtualization
This refers to the utilization of one piece of infrastructure by multiple mobile operators, especially for networks that have been densified as discussed above [2]. With many cells deployed, operators can share costs of one system instead of deploying additional infrastructure which would lead to massive consumption of energy. Small cells are a revolutionary solution not only for energy consumption but also for the Heterogenous Network Strategy (HetNet) [1]. Though low-powered and compact, small cells can have coverage of several hundred feet and can be placed in already existing infrastructure such as utility poles. Moreover, this network slicing technique creates the potential for utilizing only the required functions that are optimized and designed for specific applications. The functions can be assigned in a flexible manner to each processing node, thus employing them in the most efficient locations with regard to energy efficiency. Finally, operators can utilize hardware more efficiently by pooling gains.
Conclusion
As the Information and Communication Technology (ICT) industry makes rapid progress towards 5G networks and integrated mobile computing, there is a need to consider the impact of the envisaged system on the carbon footprint. 5G systems are expected to consume enormous amounts of energy to operate compared to the current LTE networks. Fortunately, there is a range of technologies that can be applied either consecutively or concurrently. This paper has reviewed the following possible solutions: Lean carrier design, dynamic beamforming, dynamic energy saving for small cells, densification with very small cells, advanced network sharing and network virtualization
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