2019
S.K. Goudos; M. Deruyck; D. Plets; L. Martens; K.E. Psannis; P. Sarigiannidis; W. Joseph
A Novel Design Approach for 5G Massive MIMO and NB-IoT Green Networks Using a Hybrid Jaya-Differential Evolution Algorithm Journal Article
In: IEEE Access, vol. 7, pp. 105687-105700, 2019.
Abstract | BibTeX | Tags: 4G, 5G, evolutionary algorithms, green networks, hybrid networks, Massive MIMO, NB-IoT, network design, network planning, power consumption | Links:
@article{Goudos2019105687,
title = {A Novel Design Approach for 5G Massive MIMO and NB-IoT Green Networks Using a Hybrid Jaya-Differential Evolution Algorithm},
author = { S.K. Goudos and M. Deruyck and D. Plets and L. Martens and K.E. Psannis and P. Sarigiannidis and W. Joseph},
url = {https://www.researchgate.net/publication/334778653_A_Novel_Design_Approach_for_5G_Massive_MIMO_and_NB-IoT_Green_Networks_Using_a_Hybrid_Jaya-Differential_Evolution_Algorithm},
doi = {10.1109/ACCESS.2019.2932042},
year = {2019},
date = {2019-01-01},
journal = {IEEE Access},
volume = {7},
pages = {105687-105700},
abstract = {Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results. © 2013 IEEE.},
keywords = {4G, 5G, evolutionary algorithms, green networks, hybrid networks, Massive MIMO, NB-IoT, network design, network planning, power consumption},
pubstate = {published},
tppubtype = {article}
}
2017
P. Sarigiannidis; V. Kakali; M. Fragakis
An adaptive energy-efficient framework for time-constrained optical backbone networks Journal Article
In: International Journal of Communication Systems, vol. 30, no. 3, 2017.
Abstract | BibTeX | Tags: adaptive algorithms, backbone networks, energy efficiency, optical networks, power consumption | Links:
@article{Sarigiannidis2017c,
title = {An adaptive energy-efficient framework for time-constrained optical backbone networks},
author = { P. Sarigiannidis and V. Kakali and M. Fragakis},
url = {https://www.researchgate.net/publication/275219667_An_adaptive_energy-efficient_framework_for_time-constrained_optical_backbone_networks_AN_ADAPTIVE_ENERGY-EFFICIENT_FRAMEWORK},
doi = {10.1002/dac.2972},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Communication Systems},
volume = {30},
number = {3},
abstract = {Power management has emerged as a challenge of paramount importance having strong social and financial impact in the community. The rapid growth of information and communication technologies made backbone networks a serious energy consumer. Concurrently, backbone networking is deemed as one of the most promising areas to apply energy efficient frameworks. One of the most popular energy efficient techniques, in the context of backbone networks, is to intentionally switch off nodes and links that are monitored underutilized. Having in mind that optical technology has thoroughly dominated modern backbone networks, the function of switching off techniques entails fast operation and rigorous decision-making because of the tremendous speed of the underlying optical media. This paper addresses this challenge by introducing a novel, adaptive, and efficient power management scheme for large-scale backbone networks. The proposed framework exploits traffic patterns and dynamics in order to effectively switch off the set of network entities in a periodic fashion. An adaptive decision-making algorithm is presented to maximize the network energy gains with respect to time constraints as well as QoS guarantees. The conducted simulation results reveal considerable improvements when applying the proposed framework compared with other inflexible energy efficient schemes. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.},
keywords = {adaptive algorithms, backbone networks, energy efficiency, optical networks, power consumption},
pubstate = {published},
tppubtype = {article}
}
P.G. Sarigiannidis; G. Papadimitriou; P. Nicopolitidis; E. Varvarigos
Towards power consumption in optical networks: a cognitive prediction-based technique Journal Article
In: International Journal of Communication Systems, vol. 30, no. 7, 2017.
Abstract | BibTeX | Tags: backbone networks, energy efficiency, optical networks, power consumption, prediction | Links:
@article{Sarigiannidis2017d,
title = {Towards power consumption in optical networks: a cognitive prediction-based technique},
author = { P.G. Sarigiannidis and G. Papadimitriou and P. Nicopolitidis and E. Varvarigos},
url = {https://www.researchgate.net/publication/276509099_Towards_power_consumption_in_optical_networks_A_cognitive_prediction-based_technique},
doi = {10.1002/dac.2981},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Communication Systems},
volume = {30},
number = {7},
abstract = {Modern backbone, optical networks have developed into large, massive networks, which consist of numerous intermediate and terminal nodes as well as final users. These networks serve uninterruptedly multitude of users at the expense of considerable power consumption. Many research efforts are aimed at reducing energy consumption in large-scale optical networks, however, this objective is deemed laborious: the operation of such networks has to continuously remain in good levels without disruptions. One of the most compelling techniques to remedy this situation is to switch off redundant links and devices at specific (short) periods. These links and devices remain idle as long as the network can cope with the underlying traffic demands. Hence, a power mechanism is required to manage how and when underutilized network elements may be silent during network operation. Nevertheless, this management entails fast processing and efficient decision making. While many research efforts neglect this serious factor, the problem of reducing the power consumption still threats the development of today's backbone network. In this work, an effective, cognitive power management technique is proposed by enhancing the decision making with traffic prediction. Traffic capacity is estimated in each link within the network supporting, thus, more efficient decisions on switching off underutilized or even idle network elements a priori. The technique introduced succeeds high accuracy levels, while it offers energy savings up to 30% lower than other energy-aware schemes. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.},
keywords = {backbone networks, energy efficiency, optical networks, power consumption, prediction},
pubstate = {published},
tppubtype = {article}
}
Address
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Contact Information
tel: +30 2461 056527
Email: ithaca@uowm.gr