Green energy driven cellular networks with JT CoMP technique

Jahid, Abu; Shams, Abdullah Bin; Hossain, Md. Farhad

doi:10.1016/j.phycom.2018.03.008

Cited by 33 publications

(21 citation statements)

References 32 publications

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“…Looking at the user population dynamics, we find many approaches that optimize BS operation modes for a specific (fixed) user population [1,11,14,27,34]. Jahid et al [15,16] on the other hand focus on user association specifically and on the use of on-site renewable energy sources (e.g. solar panels), and simply switch any BS into sleep mode during off-peak hours.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…3.1. In the case of operation update moments, the shadow prices y ðjÞ ðs ðkÞ Þ are updated according to update step (16) in case of a BS activation or according to update step (17) when a BS has been switched to sleep mode. In the situation where the update moment of the shadow prices and the update moment of the operation modes coincide, we first apply the shadow price updates given in Sect.…”

Section: Adjusting Shadow Prices After Operation Mode Changesmentioning

confidence: 99%

“…In the situation where the update moment of the shadow prices and the update moment of the operation modes coincide, we first apply the shadow price updates given in Sect. 3.1 and then (16) or (17).…”

Section: Adjusting Shadow Prices After Operation Mode Changesmentioning

confidence: 99%

“…Then the new shadow prices y ðjÞ ðs ðkÞ þ elÞ are given by y ðjÞ l ðs ðkÞ þ elÞ ¼ up to a given that the original shadow prices sum up to a. Moreover, from(16) we can see that for any pair fl; l 0 g of active BSs under s ðkÞ the ratios of the shadow prices are preserved.Secondly we consider the situation where we put the BS l into sleep mode. Hence we now assume that under s ðkÞ the BSl is active, and switching it into sleep mode results in operation mode vector s ðkÞ À el.…”

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confidence: 99%

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A self-organizing base station sleeping and user association strategy for dense cellular networks

2020

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Due to the rising concerns of energy consumption in wireless networks, base station (BS) sleeping strategies were introduced to save energy in low traffic scenarios. In this paper we analyse a weighted trade-off between energy consumption and user-perceived performance in dense cellular networks. We present an optimization problem representing this trade-off and derive properties of its optimal solutions. Using these properties we design a self-organizing strategy that dynamically (online) makes load-aware user association and BS operation decisions. Our strategy is self-organizing in the sense that it does not need any information or optimization beforehand, it simply relies on real-time load measurements at the BSs and user-reported SINR values. We furthermore present extensive simulation results, demonstrating the effectiveness of our self-organizing strategy and the impact of increased energy consumption on the user-perceived performance.

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Section: Discussion and Related Workmentioning

confidence: 99%

Section: Adjusting Shadow Prices After Operation Mode Changesmentioning

confidence: 99%

Section: Adjusting Shadow Prices After Operation Mode Changesmentioning

confidence: 99%

mentioning

confidence: 99%

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A self-organizing base station sleeping and user association strategy for dense cellular networks

2020

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“…It consists of actively removing CO 2 from the atmosphere into reservoirs, which can be man-made or natural. In this work we focus on the natural ones, which can be split into several types: biotic (e.g., trees); geological (e.g., underground rock formations and structures); oceanic (e.g., underwater bolsters); and underground sinks, such as saline deposits or gas reserves [68]. Each one of these is important and their main objective is to reduce the overall GHG emissions, taking advantage of natural processes to achieve that goal.…”

Section: Carbon Sequestration and Storagementioning

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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Improvement of energy efficiency by dynamic load consolidation in C‐RAN

Aktar

Anower

2022

Int J Communication

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Summary The Fifth Generation (5G) network is the next step in the evolution of cellular networks, which are anticipated to encounter a remarkable growth in traffic. To meet this traffic requirement, the network capacity must be increased, which would inevitably necessitate the expansion of additional base stations (BSs). Moreover, the expenses and energy consumption of BSs are excessively high. The highly complex signal processing in baseband units contributes to a substantial amount of energy consumption. Consequently, cloud radio access network (C‐RAN) has been demonstrated as an effective approach to overcoming 5G wireless challenges through an energy‐efficient means. C‐RAN architecture has a baseband unit (BBU) pool that consists of the number of BBUs and remote sites, which are called remote radio heads (RRHs). As a sharing of ancillary subsystems into one location, BBU pooling has a significant impact on reducing power consumption. However, C‐RAN necessitates a novel paradigm of energy consumption for resource optimization. In this article, a C‐RAN with a dynamic load consolidation algorithm depending on traffic condition is presented to jointly take the power consumption and energy efficiency targeting to decrease power consumption and increase energy efficiency. To capture the energy consumption of C‐RAN, a new energy consumption scheme considering the BBU cloud, optical link, and radio side power has been proposed that optimizes the BBU resources. Numerical results come about based on simulation settings illustrate that the presented model accomplishes an improved energy‐efficient performance with respect to the referred baseline RAN system scheme.

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Green energy driven cellular networks with JT CoMP technique

Cited by 33 publications

References 32 publications

A self-organizing base station sleeping and user association strategy for dense cellular networks

A self-organizing base station sleeping and user association strategy for dense cellular networks

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

Improvement of energy efficiency by dynamic load consolidation in C‐RAN

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