Energy Efficiency In 5G Networks Research Articles

A Comprehensive Analysis of the Impact of an Increase in User Devices on the Long-Term Energy Efficiency of 5G Networks

The global deployment of fifth-generation (5G) mobile networks, especially in urban cities, is dedicated to accommodating the demand for high data rates and reliable wireless communications. While the latest 5G networks improve service quality, the support for a simultaneous serving of more user devices (UDs) with higher data rates than previous mobile network generations will require a massive installation of different 5G base station (BS) types dominantly in urban cities. Besides contributing to the smart city service improvements, this massive installation of heterogeneous 5G BSs will also contribute to the increase in 5G network energy consumption (EC) and carbon dioxide emissions. Since this increase in installed 5G BSs imposes environmental and economic challenges, this paper analyzes the impact of the continuously rising number of 5G UDs on the energy efficiency (EE) of the radio part of Croatian and Dutch 5G networks as example cases in the period of 2020s. Analyses consider the countries’ rural, suburban, urban, and dense urban UD density areas by utilizing the proposed simulation framework for the EE evaluation of 5G heterogeneous networks (HetNet) valued through standardized mobile networks EE metrics. The study examines four proposed BS installation and operation scenarios for reducing energy costs of 5G networks that differ in optimizing energy consumption via different BS installations, sleep modes, and transmission power scaling techniques. The obtained results indicate that dynamic adaptation of BS deployments and radio resource management during operation according to the increase in the number of UDs and corresponding DVs can enhance 5G HetNet EE. The findings provide valuable insights for mobile network operators looking to optimize 5G network EE in the upcoming decade.

Hybrid Optimization Assisted Green Power Allocation Model for QoS-Driven Energy-Efficiency in 5G Networks

5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.

A New Method of Dual-Hop Energy Harvesting and Efficient Power Allocation in IoT Networks Using a Bio-Inspired Based Power Distribution Scheme

Dual hop communication is an efficient technique for the broadcasting of energy in upcoming wireless transmission particularly in the Internet of things, (IoT). Energy consumption is a major issue for 5G communication networks. Hence the main role is to constraint the power and improves the energy efficiency of 5G networks. In this paper, the power consumption is minimized to improve the energy efficiency of the 5G network. In particular, we focus on power splitting in single and multi-user cooperative communication (CC) systems where the communication between transmitter and receiver is achieved in two-phase transmission. The non-linear issue has been solved by the near to closeness expression. In the proposed scheme the Lagrange method is used to evaluate the solution to non-linearity of system energy efficiency to the finest resolution. We formulate the corresponding algorithm design as a non-convex problem by making use of Dinkel-bach algorithm and Lagrange Dual decomposition method. However, this method is more time-consuming so the non-convex problem is further optimized by using a Bio-inspired Genetic Algorithm (GA). On comparing the simulation fallouts of the proposed-scheme with the previously published work the efficiency of the proposed scheme has been observed to perform better as compared to existing ones

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks.

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Green-Oriented Dynamic Resource-on-Demand Strategy for Multi-RAT Wireless Networks Powered by Heterogeneous Energy Sources

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high ynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter $V$ and the decrease of grid power consumption cost is at the speed of $1/V$ . Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

A Utility-Based Framework for Performance and Energy-Aware Convergence in 5G Heterogeneous Network Environments

The integration of a broadcast oriented RAN architecture into a convergent framework for heterogeneous networks offers two major contributions. On the one hand, it presents an interesting solution to satisfy the exponential growth of the mobile video data traffic, and on the other hand, it is also identified as a promising key technology, that can contribute to improve the energy efficiency of 5G networks. This work proposes a novel Convergent Architecture for Broadcast, Broadband and Cellular services (CABS). This architecture is complemented with the Performance and Energy-aware Access (PEC) network selection algorithm. This joint solution offers a balanced trade-off between the user perceived QoS and the network energy efficiency in challenging heterogeneous scenarios. Furthermore, this approach guarantees the broadcast service continuity in harsh environments, and therefore, is helpful in one of the most critical scenarios for broadcasting industry: mobile and indoor reception. The proposed solution was modelled and tested in Network Simulator version 3 (NS-3), in which among other updates, a first version of an ATSC 3.0 model was introduced.

Energy-efficient 5G for a greener future

The power consumption and carbon emissions of wireless communication networks are expected to substantially increase in the 5G era. The communications industry must therefore develop strategies to optimize the energy efficiency of 5G networks, without compromising spectrum efficiency.

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

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.

Green Mobile Networks for 5G and Beyond

The heated 5G network deployment race has already begun with the rapid progress in standardization efforts, backed by the current market availability of 5G-enabled network equipment, ongoing 5G spectrum auctions, early launching of non-standalone 5G network services in a few countries, among others. In this paper, we study current and future wireless networks from the viewpoint of energy efficiency (EE) and sustainability to meet the planned network and service evolution toward, along, and beyond 5G, as also inspired by the findings of the EU Celtic-Plus SooGREEN Project. We highlight the opportunities seized by the project efforts to enable and enrich this green nature of the network as compared to existing technologies. In specific, we present innovative means proposed in SooGREEN to monitor and evaluate EE in 5G networks and beyond. Further solutions are presented to reduce energy consumption and carbon footprint in the different network segments. The latter spans proposed virtualized/cloud architectures, efficient polar coding for fronthauling, mobile network powering via renewable energy and smart grid integration, passive cooling, smart sleeping modes in indoor systems, among others. Finally, we shed light on the open opportunities yet to be investigated and leveraged in future developments.

Non-Orthogonal Radio Resource Management for RF Energy Harvested 5G Networks

Fifth generation (5G) networks are expected to support a large number of devices, provide spectral efficiency and energy efficiency. Non-orthogonal multiple access (NOMA) has been recently investigated to accommodate a large number of devices as well as spectral efficiency. On the other hand, energy efficiency in 5G networks can be addressed using energy harvesting. In this paper, we investigate NOMA in 5G networks with RF energy harvesting to maximize the number of admitted users as well as system throughput. We model a mathematical framework to optimize user grouping, power allocation, and time allocation for information transfer and energy harvesting while satisfying the minimum data rate and transmit power requirements of users. The proposed framework for optimization is a mixed integer non-linear programming problem. The mesh adaptive direct search (MADS) algorithm is adopted to find solution of the proposed framework. The MADS algorithm provides an epsilon optimal solution. The exhaustive search algorithm is used as a bench mark. Finally, the effectiveness of the proposed framework is supported by simulation results.

Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.