Demand-based radio network planning of cellular mobile communication systems

Tutschku, Kurt

doi:10.1109/infcom.1998.662915

Cited by 136 publications

(93 citation statements)

References 12 publications

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“…For instance, for optimising the coverage of mobile radio signal coverage (i.e., to optimise the network to cover more people) [340], and emergency response for aid delivery and evacuation [341] (e.g., by estimating the affected population by a flooding [342]). …”

Section: Estimating the Population In An Areamentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

628

380

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In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

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Section: Estimating the Population In An Areamentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

628

380

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“…Measuring demand has become increasingly important as mobile radio communication has become a mass communication technology. As a result, demand coverage may be converted to monetary terms and viewed as revenue coverage [26]. This has led to the development of the demand node concept (DNC), established by Tutschku and Tran-Gia [27], which is a discrete population model for expected mobile traffic description.…”

Section: Data Required For Radio Transmitter Location Decisionsmentioning

confidence: 99%

“…Based on the land use of an area, the spatial traffic distribution may be derived using complex estimation methods, and be stored in a traffic matrix. From this traffic matrix, the demand nodes may then be generated using a clustering method [26].…”

Section: Data Required For Radio Transmitter Location Decisionsmentioning

confidence: 99%

Optimisation of Radio Transmitter Locations in Mobile Telecommunication Networks

Schmidt-Dumont

Vuuren

2016

SAJIE

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Multiple factors have to be taken into account when mobile telecommunication network providers make decisions about radio transmitter placement. Generally, area coverage and the average signal level provided are of prime importance in these decisions. These criteria give rise to a bi-objective problem of facility location, with the goal of achieving an acceptable trade-off between maximising the total area coverage and maximising the average signal level provided to the demand region by a network of radio transmitters. This paper establishes a mathematical modelling framework, based on these two placement criteria, for evaluating the effectiveness of a given set of radio transmitter locations. In the framework, coverage is measured according to the degree of obstruction of the so-called 'Fresnel zone' that is formed between handset and base station, while signal strength is modelled taking radio wave propagation loss into account. This framework is used to formulate a novel bi-objective facility location model that may form the basis for decision support aimed at identifying high-quality transmitter location trade-off solutions for mobile telecommunication network providers. But it may also find application in various other contexts (such as radar, watchtower, or surveillance camera placement optimisation). OPSOMMINGVerskeie faktore moet in ag geneem word wanneer radiosenderplasingsbesluite deur mobiele telekommunikasienetwerke gemaak word. In die algemeen word area-oordekking en gemiddelde seinsterkte in hierdie besluite as belangrike kriteria geag. Hierdie kriteria gee aanleiding tot 'n twee-doelige fasiliteitplasingsprobleem gemik op die soeke na aanvaarbare afruilings tussen die maksimering van totale area-oordekking en die maksimering van gemiddelde seinsterkte aan gebiede waarin aanvraag na oordekking bestaan. 'n Wiskundige modelleringsraamwerk, gebaseer op hierdie twee plasingskriteria, word in hierdie artikel vir die evaluering van 'n versameling radiosenderliggings daargestel. In die raamwerk word oordekking gemeet volgens die mate waartoe die Fresnel-sone tussen die handtoestel en die basisstasie belemmer is, terwyl seinsterkte aan die hand van radiogolf-voortsettingsverliese gemodelleer word. Hierdie raamwerk word gebruik om 'n nuwe, twee-doelige fasiliteitplasingsmodel te formuleer wat as basis kan dien vir besluitsteun in die soeke na hoë-kwaliteit senderliggingsafruilings deur mobiele telekommunikasie-netwerkverskaffers, maar vind ook toepassing in verskeie ander kontekste (soos die optimale plasing van radars, uitkyktorings, of waarnemingskameras).

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“…Base station positioning has been studied extensively in the past, using simulated annealing [2,3], evolutionary algorithms [4], linear programming [5], and greedy algorithms [6,7]. Other work has explored the trade-offs between coverage, cell count and capacity [8].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Self-deployment of Wireless Access Networks in an Airport Environment

Claußen¹

2006

Lecture Notes in Computer Science

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Abstract. In environments with highly dynamic user demand, for example in airports, high over-dimensioning of wireless access networks is required to be able to serve high user densities at any possible location in the covered area, resulting in a large number of base stations. This problem is addressed with the novel concept of a self-deploying network. Distributed algorithms are proposed, which autonomously identify the need of changes in position and configuration of wireless access nodes and adapt the network to its environment. It is shown that a self-deploying network can significantly reduce the number of required base stations compared to a conventional statically deployed network. In this paper, this is demonstrated in a specific test scenario at Athens International Airport, simulating a moving user hotspot after the arrival of an airplane.

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Demand-based radio network planning of cellular mobile communication systems

Cited by 136 publications

References 12 publications

Applications of 3D City Models: State of the Art Review

Applications of 3D City Models: State of the Art Review

Optimisation of Radio Transmitter Locations in Mobile Telecommunication Networks

Autonomous Self-deployment of Wireless Access Networks in an Airport Environment

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