FIGURE 16 - available via license: Creative Commons Attribution 4.0 International
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The range of results using the data-based model for the DU (top) and SU (bottom) areas with 2025 forecasted data, taking uncertainty into account
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Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era...
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With telecommunications connections, data transfer capacity will increase and enable new services and products in growing markets, just as has happened with the development of 1G, 2G, 3G and 4G. New ITC-based technologies, combined with high-speed telecommunications networks, enable new products and services, supply chains and new business models....
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Five decades have passed since the first bit was transmitted over the internet. Although the internet has improved our lives and led to the digital economy, currently only 51% of the world’s population have access to it. Currently, consumers mostly access the internet via mobile broadband, 2G, 3G, and 4G services. Regulatory bodies such as the Fede...
Citations
... The data from [33] is also the basis for [35], which presents field measurements on data and visitor volumes. Combining these with parameters for different RATs (Radio Access Technologies), including 5G RUs from Nokia product data sheets, they calculate and extrapolate the base station power consumption in dense urban and suburban areas of Finland. ...
... The discussed models lack good open 5G data. They use 4G data [33], normalize the power consumption values [34] (to maintain commercial security) or speculate on the power dynamics of 5G hardware based on manufacturer reported spectral efficiency [35]. This demonstrates a lack of open research with clear reporting of empirical power consumption within real networks. ...
This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency, power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially, the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful comparison between the energy savings from AI techniques and their associated energy costs. Finally, we discuss future research opportunities to uncover the utility of AI for energy saving.
... Similar analysis for Finland shows that the relatively high energy consumption of the legacy 3G network means it remains a large proportion of total energy consumption even as workloads transition to newer 4G and 5G networks (Huttunen et al., 2023). ...
It is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real‐world network energy consumption.
... Furthermore, as the density of deployments in urban and suburban areas increases, the importance of energy efficiency becomes even more pronounced in sustaining 5G network operations. This is where the research conducted by Huttunen et al. in [20] becomes significant. Their work focuses on energy efficiency in dense deployments, exploring protocols and management techniques aimed at reducing the environmental impact of 5G technology. ...
5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-
machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hotspot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested
model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and
bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G.
... The data from [33] is also the basis for [35], which presents field measurements on data and visitor volumes. Combining these with parameters for different RATs (Radio Access Technologies), including 5G RUs from Nokia product data sheets, they calculate and extrapolate the base station power consumption in dense urban and suburban areas of Finland. ...
... The discussed models lack good open 5G data. They use 4G data [33], normalize the power consumption values [34] (to maintain commercial security) or speculate on the power dynamics of 5G hardware based on manufacturer reported spectral efficiency [35]. This demonstrates a lack of open research with clear reporting of empirical power consumption within real networks. ...
This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency, power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially, the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful comparison between the energy savings from AI techniques and their associated energy costs. Finally, we discuss future research opportunities to uncover the utility of AI for energy saving.
Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max–min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max–min planning investigates energy efficiency only from the links/nodes perspective. The max–min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max–min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max–min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.
