Energy-Efficient Switching ON/OFF Strategies Analysis for Dense Cellular Networks With Partial Conventional Base-Stations

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache-hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul-aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache-hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed-form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.

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5G-Enabled UAVs with Command and Control Software Component at the Edge for Supporting Energy Efficient Opportunistic Networks

Energies ◽

10.3390/en14051480 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1480

Author(s):

Harilaos Koumaras ◽

George Makropoulos ◽

Michael Batistatos ◽

Stavros Kolometsos ◽

Anastasios Gogos ◽

...

Keyword(s):

Energy Efficiency ◽

Real Time ◽

Cellular Networks ◽

Flight Control ◽

Energy Efficient ◽

Opportunistic Networks ◽

Base Stations ◽

Proof Of Concept ◽

High Definition ◽

5G Network

Recently Unmanned Aerial Vehicles (UAVs) have evolved considerably towards real world applications, going beyond entertaining activities and use. With the advent of Fifth Generation (5G) cellular networks and the number of UAVs to be increased significantly, it is created the opportunity for UAVs to participate in the realisation of 5G opportunistic networks by carrying 5G Base-Stations to under-served areas, allowing the provision of bandwidth demanding services, such as Ultra High Definition (UHD) video streaming, as well as other multimedia services. Among the various improvements that will drive this evolution of UAVs, energy efficiency is considered of primary importance since will prolong the flight time and will extend the mission territory. Although this problem has been studied in the literature as an offline resource optimisation problem, the diverse conditions of a real UAV flight does not allow any of the existing offline optimisation models to be applied in real flight conditions. To this end, this paper discusses the amalgamation of UAVs and 5G cellular networks as an auspicious solution for realising energy efficiency of UAVs by offloading at the edge of the network the Flight Control System (FCS), which will allow the optimisation of the UAV energy resources by processing in real time the flight data that have been collected by onboard sensors. By exploiting the Multi-access Edge Computing (MEC) architectural feature of 5G as a technology enabler for realising this offloading, the paper presents a proof-of-concept implementation of such a 5G-enabled UAV with softwarized FCS component at the edge of the 5G network (i.e., the MEC), allowing by this way the autonomous flight of the UAV over the 5G network by following control commands mandated by the FCS that has been deployed at the MEC. This proof-of-concept 5G-enabled UAV can support the execution of real-time resource optimisation techniques, a step-forward from the currently offline-ones, enabling in the future the execution of energy-efficient and advanced missions.

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Energy-Efficient On–Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks

Applied Sciences ◽

10.3390/app6110356 ◽

2016 ◽

Vol 6 (11) ◽

pp. 356 ◽

Cited By ~ 2

Author(s):

Woongsup Lee ◽

Bang Jung

Keyword(s):

Power Control ◽

Cellular Networks ◽

Energy Efficient ◽

Base Stations ◽

Cell Base

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Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey

IEEE Communications Surveys & Tutorials ◽

10.1109/comst.2015.2403395 ◽

2015 ◽

Vol 17 (2) ◽

pp. 803-826 ◽

Cited By ~ 200

Author(s):

Jingjin Wu ◽

Yujing Zhang ◽

Moshe Zukerman ◽

Edward Kai-Ning Yung

Keyword(s):

Cellular Networks ◽

Energy Efficient ◽

Base Stations ◽

Sleep Mode

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Energy efficient mobile edge computing in dense cellular networks

2017 IEEE International Conference on Communications (ICC) ◽

10.1109/icc.2017.7997128 ◽

2017 ◽

Cited By ~ 22

Author(s):

Lixing Chen ◽

Sheng Zhou ◽

Jie Xu

Keyword(s):

Cellular Networks ◽

Energy Efficient ◽

Edge Computing ◽

Mobile Edge Computing

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Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽

10.3390/s21134618 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4618

Author(s):

Francisco Oliveira ◽

Miguel Luís ◽

Susana Sargento

Keyword(s):

Machine Learning ◽

Cellular Networks ◽

Real Data ◽

Emerging Technology ◽

Machine Learning Algorithms ◽

Base Stations ◽

Aerial Vehicle ◽

Positioning Algorithm ◽

The Military ◽

Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

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Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00232-y ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Zhuofan Liao ◽

Jingsheng Peng ◽

Bing Xiong ◽

Jiawei Huang

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Cellular Networks ◽

Physical Distance ◽

Edge Computing ◽

Base Stations ◽

Mobile Edge Computing ◽

Total Energy Consumption ◽

Average Delay ◽

Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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