Energy Efficient Backhauling for 5G Small Cell Networks

Mowla, Munjure; Ahmad, Iftekhar; Habibi, Daryoush; Phung, Quoc Viet

doi:10.1109/tsusc.2018.2838116

Cited by 40 publications

(26 citation statements)

References 57 publications

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“…Data-aware mechanisms have also been focused on, relying once again on BS cooperation in order to optimize the overall cell zooming process, and thus, the EE of the whole network 25. Other mechanisms have also been proposed in [51][52][53][54][55][56][57][58].…”

Section: Ran Edge and Ee Methodsmentioning

confidence: 99%

One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting

et al. 2020

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Fifth generation (5G) and Beyond-5G (B5G) will be characterized by highly dense deployments, both on network plane and user plane. Internet of Things, massive sensor deployments and base stations will drive even more energy consumption. User behavior towards mobile service usage is witnessing a paradigm shift with heavy capacity, demanding services resulting in an increase of both screen time and data transfers, which leads to additional power consumption. Mobile network operators will face additional energetic challenges, mainly related to power consumption and network sustainability, starting right in the planning phase with concepts like energy efficiency and greenness by design coming into play. The main contribution of this work is a two-tier method to address such challenges leading to positively-offset carbon dioxide emissions related to mobile networks using a novel approach. The first tier contributes to overall power reduction and optimization based on energy efficient methods applied to 5G and B5G networks. The second tier aims to offset the remaining operational power usage by completely offsetting its carbon footprint through geosequestration. This way, we show that the objective of minimizing overall networks’ carbon footprint is achievable. Conclusions are drawn and it is shown that carbon sequestration initiatives or program adherence represent a negligible cost impact on overall network cost, with the added value of greener and more environmentally friendly network operation. This can also relieve the pressure on mobile network operators in order to maximize compliance with environmentally neutral activity.

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Section: Ran Edge and Ee Methodsmentioning

confidence: 99%

One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting

et al. 2020

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“…Firstly, the use of multiple antenna technology at the MBS to support in-band wireless BH connections to each LPN has been investigated in [27]- [35]. Secondly, a cluster-based multi-hop wireless BH technology, where the LPNs are divided into several clusters with a head that connects to the MBS, has been explored in [14], [15], [36]- [39]. In the cluster-based architecture, LPNs connect to the MBS through the cluster head either directly or through one or more neighboring LPNs.…”

Section: A Related Workmentioning

confidence: 99%

“…Moreover, the authors did not consider realistic channel impairments like rain attenuation and atmospheric gaseous effects on the mmWave signal propagation. Only the works in [15] and [36] considered the problem of flow control for the multi-hop mmWave BH HetNet. As mentioned in [15], one important suggestion can be to randomly re-select some BH links from the set of available links and shift between the links with higher payoff (which can be minimum power consumption or highest throughput).…”

Section: A Related Workmentioning

confidence: 99%

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Energy Efficient User Association, Power, and Flow Control in Millimeter Wave Backhaul Heterogeneous Networks

Aboagye

Ibrahim

Ngatched

2020

IEEE Open J. Commun. Soc.

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This paper studies the problem of energy efficiency (EE) maximization via user association, power, and backhaul (BH) flow control in the downlink of millimeter wave BH heterogeneous networks. This problem is mathematically formulated as a mixed-integer non-linear program, which is non-convex. To get a tractable solution, the initial problem is separated into two sub-problems and optimized sequentially. The first is a joint user association and power control sub-problem for the access network (AN) (AN sub-problem). The second is a joint flow and power control sub-problem for the BH network (BH sub-problem). While the BH sub-problem is a convex optimization problem and hence can be efficiently solved, the AN sub-problem assumes the form of a generalized assignment problem, which is known to be NP-hard. To that end, we utilize Lagrangian decomposition to propose two polynomial time solution techniques that obtain a high-quality solution for the AN sub-problem. The first, referred to as Technique A, uses dynamic programming, the subgradient method, and a heuristic. The second, named Technique B, uses the multiplier adjustment method, the sorting algorithm, and a heuristic. Simulation results are used to demonstrate the effectiveness of the proposed energy efficient user association, power, and BH flow control algorithms as compared with benchmark user association schemes that incorporate the BH sub-problem algorithm, in terms of the total AN power, BH power, and overall network (AN plus BH) EE. The computational complexity and practical implementation of the proposed algorithms are discussed. INDEX TERMS Flow control, generalized assignment problem, Lagrangian multipliers, multiplier adjustment method, power control, subgradient, user association.

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“…Lastly, dedicated glial cells exist, with microglia being their peculiar type, having the ability to repair damaged neurons. Observations of all these features and capabilities of the human body (with the brain and the nervous system being their primary focus) lead to new proposals concerning their implementation in the context of dense wireless networks [1], [2]. In such a case, numerous transmission points (nodes), deployed randomly over the area concerned, transmit with relatively low power rating, thus communicating with their closets neighbors.…”

Section: Introductionmentioning

confidence: 99%

Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks

Kułacz¹,

Kliks

2019

JTIT

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This paper presents developments in the area of brain-inspired wireless communications relied upon in dense wireless networks. Classic approaches to network design are complemented, firstly, by the neuroplasticity feature enabling to add the learning ability to the network. Secondly, the microglia ability enabling to repair a network with damaged neurons is considered. When combined, these two functionalities guarantee a certain level of fault-tolerance and self-repair of the network. This work is inspired primarily by observations of extremely energy efficient functions of the brain, and of the role that microglia cells play in the active immune defense system. The concept is verified by computer simulations, where messages are transferred through a dense wireless network based on the assumption of minimized energy consumption. Simulation encompasses three different network topologies which show the impact that the location of microglia nodes and their quantity exerts on network performance. Based on the results achieved, some algorithm improvements and potential future work directions have been identified.

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Energy Efficient Backhauling for 5G Small Cell Networks

Cited by 40 publications

References 57 publications

One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting

One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting

Energy Efficient User Association, Power, and Flow Control in Millimeter Wave Backhaul Heterogeneous Networks

Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks

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