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

Energies ◽  
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.

scholarly journals Energy Efficient Caching in Backhaul-Aware Cellular Networks with Dynamic Content Popularity

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jiequ Ji ◽  
Kun Zhu ◽  
Ran Wang ◽  
Bing Chen ◽  
Chen Dai
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Energy Efficient ◽  
Base Stations ◽  
Noise Model ◽  
Traffic Demand ◽  
Hit Rate ◽  
Dynamic Content ◽  
Content Popularity ◽  
Cache Capacity

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.

scholarly journals Coverage and Energy Efficiency Analysis for Two-Tier Heterogeneous Cellular Networks Based on Matérn Hard-Core Process

2019 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Jie Yang ◽  
Ziyu Pan ◽  
Lihong Guo
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Mutual Exclusion ◽  
Interference Management ◽  
Hard Core ◽  
Approximate Expression ◽  
Base Stations ◽  
Heterogeneous Cellular Networks ◽  
Coverage Analysis ◽  
Management Techniques

Due to the dense deployment of base stations (BSs) in heterogeneous cellular networks (HCNs), the energy efficiency (EE) of HCN has attracted the attention of academia and industry. Considering its mathematical tractability, the Poisson point process (PPP) has been employed to model HCNs and analyze their performance widely. The PPP falls short in modeling the effect of interference management techniques, which typically introduces some form of spatial mutual exclusion among BSs. In PPP, all the nodes are independent from each other. As such, PPP may not be suitable to model networks with interference management techniques, where there exists repulsion among the nodes. Considering this, we adopt the Matérn hard-core process (MHCP) instead of PPP, in which no two nodes can be closer than a repulsion radius from one another. In this paper, we study the coverage performance and EE of a two-tier HCN modelled by Matérn hard-core process (MHCP); we abbreviate this kind of two-tier HCN as MHCP-MHCP. We first derive the approximate expression of coverage probability of MHCP-MHCP by extending the approximate signal to interference ratio analysis based on the PPP (ASAPPP) method to multi-tier HCN. The concrete SIR gain of the MHCP model relative to the PPP model is derived through simulation and data fitting. On the basis of coverage analysis, we derive and formulate the EE of MHCP-MHCP network. Simulation results verify the correctness of our theoretical analysis and show the performance difference between the MHCP-MHCP and PPP modelled network.

scholarly journals Cell Throughput based Sleep Control Scheme for Heterogeneous Cellular Networks

2018 ◽  
Vol 12 (1) ◽  
pp. 26-33
Author(s):  
Prapassorn Phaiwitthayaphorn ◽  
Kazuo Mori ◽  
Hideo Kobayashi ◽  
Pisit Boonsrimuang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Cellular Networks ◽  
Operating Cost ◽  
Reducing Power ◽  
Network Capacity ◽  
Base Station ◽  
Base Stations ◽  
Improve Energy Efficiency ◽  
Sleep Control

The mobile traffic continuously grows at a rapid rate driven by the widespread use of wireless devices. Along with that, the demands for higher data rate and better coverage lead to increase in power consumption and operating cost of network infrastructure. The concept of heterogeneous networks (HetNets) has been proposed as a promising approach to provide higher coverage and capacity for cellular networks. HetNet is an advanced network consisting of multiple kinds of base stations, i.e., macro base station (MBS), and small base station (SBS). The overlay of many SBSs into the MBS coverage can provide higher network capacity and better coverage in cellular networks. However, the dense deployment of SBSs would cause an increase in the power consumption, leading to a decrease in the energy efficiency in downlink cellular networks. Another technique to improve energy efficiency while reducing power consumption in the network is to introduce sleep control for SBSs. This paper proposes cell throughput based sleep control which the cell capacity ratio for the SBSs is employed as decision criteria to put the SBSs into a sleep state. The simulation results for downlink communications demonstrate that the proposed scheme improves the energy efficiency, compared with the conventional scheme.

scholarly journals Analysis of Pel Decimation and Technology Choices to Reduce Energy on SAD Calculation

2014 ◽  
Vol 9 (1) ◽  
pp. 48-59
Author(s):  
Ismael Seidel ◽  
André Beims Bräscher ◽  
Bruno George De Moraes ◽  
Marcio Monteiro ◽  
José Luis Güntzel
Keyword(s):  
Energy Efficiency ◽  
Video Coding ◽  
Energy Efficient ◽  
High Definition ◽  
Maximum Throughput ◽  
Vlsi Architectures ◽  
Quality Costs ◽  
Coding Standards ◽  
Power And Energy ◽  
Configurable Architecture

As the number of pixels per frame tends to increase in new high definition video coding standards such as HEVC and VP9, pel decimation appears as a viable means of increasing the energy efficiency of Sum of Absolute Differences (SAD) calculation. First, we analyze the quality costs of pel decimation using a video coding software. Then we present and evaluate two VLSI architectures to compute the SAD of 4x4 pixel blocks: one that can be configured with 1:1, 2:1 or 4:1 sampling ratios and a non-configurable one, to serve as baseline in comparisons. The architectures were synthesized for 90nm, 65nm and 45nm standard cell libraries assuming both nominal and Low-Vdd/High-Vt (LH) cases for maximum and for a given target throughput. The impacts of both subsampling and LH on delay, power and energy efficiency are analyzed. In a total of 24 syntheses, the 45nm/LH configurable SAD architecture synthesis achieved the highest energy efficiency for target throughput when operating in pel decimation 4:1, spending only 2.05pJ for each 4×4 block. This corresponds to about 13.65 times less energy than the 90nm/nominal configurable architecture operating in full sampling mode and maximum throughput and about 14.77 times less than the 90nm/nominal non-configurable synthesis for target throughput. Aside the improvements achieved by using LH, pel decimation solely was responsible for energy reductions of 40% and 60% when choosing 2:1 and 4:1 subsampling ratios, respectively, in the configurable architecture. Finally, it is shown that the configurable architecture is more energy-efficient than the non-configurable one.

scholarly journals Stochastic Geometry Analysis of Downlink Spectral and Energy Efficiency in Ultradense Heterogeneous Cellular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jiaqi Lei ◽  
Hongbin Chen ◽  
Feng Zhao
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Stochastic Geometry ◽  
Density Ratio ◽  
Base Station ◽  
Small Cell ◽  
Base Stations ◽  
Heterogeneous Cellular Networks ◽  
Downlink Throughput ◽  
The Impact

The energy efficiency (EE) is a key metric of ultradense heterogeneous cellular networks (HCNs). Earlier works on the EE analysis of ultradense HCNs by using the stochastic geometry tool only focused on the impact of the base station density ratio and ignored the function of different tiers. In this paper, a two-tier ultradense HCN with small-cell base stations (SBSs) and user equipments (UEs) densely deployed in a traditional macrocell network is considered. Firstly, the performance of the ultradense HCN in terms of the association probability, average link spectral efficiency (SE), average downlink throughput, and average EE is theoretically analyzed by using the stochastic geometry tool. Then, the problem of maximizing the average EE while meeting minimum requirements of the average link SE and average downlink throughput experienced by UEs in macrocell and small-cell tiers is formulated. As it is difficult to obtain the explicit expression of average EE, impacts of the SBS density ratio and signal-to-interference-plus-noise ratio (SINR) threshold on the network performance are investigated through numerical simulations. Simulation results validate the accuracy of theoretical results and demonstrate that the maximum value of average EE can be achieved by optimizing the SBS density ratio and the SINR threshold.

scholarly journals Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1059 ◽  
Author(s):  
Wenzhe Guo ◽  
Hasan Erdem Yantır ◽  
Mohammed E. Fouda ◽  
Ahmed M. Eltawil ◽  
Khaled Nabil Salama
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Real Time ◽  
Classification Accuracy ◽  
Energy Efficient ◽  
Spiking Neural Networks ◽  
Neuron Network ◽  
Neuromorphic Hardware ◽  
Neuromorphic Systems ◽  
Network Compression

To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the fact that neurons that fire more spikes contribute more to network performance. Based on these criteria, we demonstrate that pruning with an adaptive spike count threshold provides a simple and effective approach that can reduce network size significantly and maintain high classification accuracy. The online adaptive pruning shows potential for developing energy-efficient training techniques due to less memory access and less weight-update computation. Furthermore, a parallel digital implementation scheme is proposed to implement spiking neural networks (SNNs) on field programmable gate array (FPGA). Notably, our proposed pruning strategies preserve the dense format of weight matrices, so the implementation architecture remains the same after network compression. The adaptive pruning strategy enables 2.3× reduction in memory size and 2.8× improvement on energy efficiency when 400 neurons are pruned from an 800-neuron network, while the loss of classification accuracy is 1.69%. And the best choice of pruning percentage depends on the trade-off among accuracy, memory, and energy. Therefore, this work offers a promising solution for effective network compression and energy-efficient hardware implementation of neuromorphic systems in real-time applications.

Energy-Efficient Resource Allocation in CoMP Systems with Backhaul Capacity Constraints

2014 ◽  
Vol 716-717 ◽  
pp. 1312-1317
Author(s):  
Qi Wen Wan ◽  
Xiang Ming Wen ◽  
Zhao Ming Lu ◽  
Hai Jun Zhang ◽  
Jun Zhao
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Energy Efficient ◽  
Scheduling Algorithm ◽  
Mobile Station ◽  
Capacity Constraints ◽  
Base Stations ◽  
Coordinated Multipoint ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation

Energy efficient resource allocation is one of the critical issues for the coordinated multipoint (CoMP) transmission system, where the signal received by each mobile station needs to be converged among multiple base stations (BSs) in the same cluster. However, as the interaction of the system is frequent, the overhead of the backhaul signaling will be enormous. In this paper, an iterative resource allocation and scheduling algorithm (IRASA) with dual method is proposed to solve the optimization problem aiming to maximize the system energy efficiency, which considers the backhaul capacity constraints, circuit power consumption and zero forcing pre-coding (ZFP). Simulation results show that our proposed IRASA can significantly improve the energy efficiency of the system with the backhaul capacity constraints.

Sign in / Sign up

Export Citation Format

Share Document