Small-Cell Self-Organizing Wireless Networks

Fehske, Albrecht; Viering, Ingo; Voigt, Jens; Sartori, Cinzia; Redana, Simone; Fettweis, Gerhard

doi:10.1109/jproc.2014.2301595

Cited by 81 publications

(54 citation statements)

References 21 publications

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“…In addition, it is important to note that, typically, a given SBS is classified as a microcell, picocell, or femtocell BS, and that in recent studies, the use of increasingly small BSs has become a notable trend [2]. As a result, femtocell BSs were chosen as the SBSs used in this study.…”

Section: Resultsmentioning

confidence: 99%

“…By allowing for the efficient utilization of spectrum resources, which in turn allows for increased network capacity and is thus key to enhancing the energy efficiency of networks, the use of SBSs makes it easier for network operators to meet the requirements of network users. In other words, for operators seeking to ensure efficient transmission in the context of modern wireless network environments, it is generally understood that the utilization of a two-layer cellular network architecture (that is, small cell areas incorporated into a larger macrocell area) is key [2,3].…”

Section: Introductionmentioning

confidence: 99%

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An Energy-Efficient Coverage Algorithm for Macrocell—Small Cell Network Systems

Chung

2017

Energies

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Abstract:The energy efficiency of a macrocell base station (MBS) can be substantially improved via the deployment of small cell base stations (SBSs) within the coverage area of the MBS. Moreover, this approach is expected to remain a key feature of communication network markets in the future. However, little research has been conducted to uncover effective solutions to the issue of coverage holes (i.e., specific locations or areas in which a user is not able to get an adequate signal from the wireless network) that may occur in the context of such a network architecture. To address this dearth of relevant research, the present study proposes an energy-efficient coverage algorithm utilizing novel system configurations, for use in such macrocell-small cell network systems. The goal of the proposed algorithm is providing the maximum possible reduction of the combined power consumed by the transceivers of all the BSs (that is, the SBSs and the MBS), while simultaneously guaranteeing the provision of comprehensive wireless signal coverage to users under various scenarios. In order to accomplish this aim in an efficient manner, the algorithm smartly adjusts the power levels of all the SBSs in a given system, including the full deactivation of a previously active SBS, or the activation of a previously inactive SBS, according to the dynamics of the given network traffic, thereby modifying their power consumption as necessary. The results for simulations of various test scenarios indicated that the algorithm exhibits better performance than two conventional methods in terms of its overall effects on coverage, power usage, and average transmission rate. The simulated power savings yielded by the proposed algorithm were particularly notable, as it garnered an improvement as high as 78% under the condition of light traffic volumes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Coverage Algorithm for Macrocell—Small Cell Network Systems

Chung

2017

Energies

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“…In addition, the authors in [4], [6] have extended the usage of the modified Shannon's formula to several multiantenna schemes and bandwidth values for SL simulations.…”

Section: A Related Workmentioning

confidence: 99%

“…Although MIESM reduces the simulation time compared to a complete implementation of the PHY chain, other approaches have been proposed to further reduce the run time of systems level simulations. For instance, the popular "modified Shannon's formula", proposed by Mogensen et al [3], is often used to assess the spectral efficiency of Orthogonal Frequency Division Multiple Access (OFDMA)-based systems in SL simulators [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Accuracy vs. complexity trade-off in simulations of future wireless networks

Galiotto

Crowley

Marchetti

et al. 2015

2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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As wireless networks shift towards denser deployments, simulations take longer time to run and consume more resources. Simulation of the physical (PHY) layer is particularly time consuming and, as such, its implementation requires the use of abstracted models with low computational complexity, in order to provide accurate results in reasonable time. Although Mutual Information Effective SINR Mapping (MIESM) is commonly used as the standard method to abstract the PHY layer, the popular "modified Shannon's equation"-proposed by Mogensen et al.-has been gaining ground as a simpler and lighter implementation of the PHY layer in System Level (SL) simulations. This paper aims at comparing MIESM and the modified Shannon's formula for SL simulators in terms of accuracy and computational complexity. Results show that Shannon's method proves beneficial for networks with a large number of users, as it is less complex and allows us to reduce up to 30 times the simulation time required by the PHY layer abstraction.On the other hand, the accuracy is in the range of 80-87% for the instantaneous throughput compared to MIESM; the accuracy improves up to 95% for the time-averaged user throughput. In scenarios involving fast-varying channels, the modified Shannon's formula needs to be calibrated considering the actual user speed; in this case, accuracy of 90% can be achieved for a user speed of 60kmph.Index Terms-Shannon, MIESM, computational complexity, accuracy, simulator, system level, dense networks.

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“…It is expected that significant improvements in the energy-efficiency of cellular networks can be accomplished through approaches which must be jointly combined. Such approaches take into account: the dynamic management of wireless network resources through access points/BSs sleep modes or cell zooming techniques [9][10][11], improvements in the energy-efficiency of BSs hardware [12], a self-organizing network (SON) concept [13] or the implementation of RES for powering BSSs [14].…”

Section: Related Workmentioning

confidence: 99%

Performance Analyses of Renewable and Fuel Power Supply Systems for Different Base Station Sites

2014

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Base station sites (BSSs) powered with renewable energy sources have gained the attention of cellular operators during the last few years. This is because such "green" BSSs impose significant reductions in the operational expenditures (OPEX) of telecom operators due to the possibility of on-site renewable energy harvesting. In this paper, the green BSSs power supply system parameters detected through remote and centralized real time sensing are presented. An implemented sensing system based on a wireless sensor network enables reliable collection and post-processing analyses of many parameters, such as: total charging/discharging current of power supply system, battery voltage and temperature, wind speed, etc. As an example, yearly sensing results for three different BSS configurations powered by solar and/or wind energy are discussed in terms of renewable energy supply (RES) system performance. In the case of powering those BSS with standalone systems based on a fuel generator, the fuel consumption models expressing interdependence among the generator load and fuel consumption are proposed. This has allowed energy-efficiency comparison of the fuel powered and RES systems, which is presented in terms of the OPEX and carbon dioxide (CO2) reductions. Additionally, approaches based on different BSS air-conditioning systems and the on/off regulation of a daily fuel generator activity are proposed and validated in terms of energy and capital expenditure (CAPEX) savings. OPEN ACCESSEnergies 2014, 7 7817

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Small-Cell Self-Organizing Wireless Networks

Cited by 81 publications

References 21 publications

An Energy-Efficient Coverage Algorithm for Macrocell—Small Cell Network Systems

An Energy-Efficient Coverage Algorithm for Macrocell—Small Cell Network Systems

Accuracy vs. complexity trade-off in simulations of future wireless networks

Performance Analyses of Renewable and Fuel Power Supply Systems for Different Base Station Sites

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