ICEPT-an integrated cellular network planning tool

Tutschku, Kurt; Leibnitz, Kenji; Tran-Gia, Phuoc

doi:10.1109/vetec.1997.600432

Cited by 36 publications

(26 citation statements)

References 4 publications

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“…Most works target cellular networks and jointly optimize the placement (and transmission power) of base stations [26], [27], [34]- [36]. Our work differs in that we target spectrum markets where spectrum users come from independent entities with unplanned locations.…”

Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

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Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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Abstract-Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs.At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

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Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

“…Second, our work targets dynamic spectrum markets where users are at unplanned places and the resulting conflict graph can be of arbitrary shape. This is fundamentally different from cellular networks [26], [27], [34]- [36] that optimize the placement (and transmit power) of base stations to produce conflict graphs of specific shapes.…”

Section: Introductionmentioning

confidence: 99%

Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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“…In [33] the modelling approach that describes the traffic distribution in terms of demand nodes is firstly proposed. Demand nodes (or test points) represent the center of an area characterized by a given traffic demand (usually expressed in Erlang).…”

Section: Related Workmentioning

confidence: 99%

Radio planning and coverage optimization of 3G cellular networks

2007

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Radio planning and coverage optimization are critical issues for service providers and vendors that are deploying third generation mobile networks and need to control coverage as well as the huge costs involved. Due to the peculiarities of the Code Division Multiple Access (CDMA) scheme used in 3G cellular systems like UMTS and CDMA2000, network planning cannot be based only on signal predictions, and the approach relying on classical set covering formulations adopted for second generation systems is not appropriate.In this paper we investigate mathematical programming models for supporting the decisions on where to install new base stations and how to select their configuration (antenna height and tilt, sector orientations, maximum emission power, pilot signal, etc.) so as to find a trade-off between maximizing coverage and minimizing costs. The overall model takes into account signal-quality constraints in both uplink and downlink directions, as well as the power control mechanism and the pilot signal.Preliminary results have been presented in [5,7,8].Since even small and simplified instances of this NP-hard problem are beyond the reach of state-of-the-art techniques for mixed integer programming, we propose a Tabu Search algorithm which provides good solutions within a reasonable computing time. Computational results obtained for realistic instances, generated according to classical propagation models, with different traffic scenarios (voice and data) are reported and discussed.

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“…Among the network information that our implementation can measure we cite: cell identifier code, Time Advance For the network extension, i.e. positioning new base stations in positions that are not covered by the exiting network, a similar approach to the one presented in [1] is adopted. Here, our contribution is to correct the demand node map given in input by real measurements done with our platform and applied in some measurement points (Figure 3).…”

Section: Implemented Modules For Cellular Networkmentioning

confidence: 99%

PCNM: A New Platform for Cellular Networks Measurements and Optimization

Lemlouma

Lefebvre

Cespedes

2007

2007 International Conference on Wireless Communications, Networking and Mobile Computing

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Abstract-In this paper, we present PCNM, a new mobile platform for cellular networks measurements. PCNM is based on a set of techniques that tailors theoretical calculations and simulations to the real cellular network environment. It includes: (a) modules that measure different parameters of a base station (BS) such as localization, cells identification, time advance information, reception level and quality, (b) a new protocol that optimizes the task of network measurement by monitoring a set of mobile nodes and finally (c) the ability to extend an existing cellular network by adding new base stations. We evaluate our genetic algorithm used to reduce the nodes mobility and optimize the measurement extraction of N base stations using k mobile sensors (k ≥ 1). We show how connecting real measurements (using mobile sensors in a collaborative way) to theoretical and prediction methods is of high benefits for cellular networks maintenance, extension and performances evaluation.

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ICEPT-an integrated cellular network planning tool

Cited by 36 publications

References 4 publications

Practical Conflict Graphs in the Wild

Practical Conflict Graphs in the Wild

Radio planning and coverage optimization of 3G cellular networks

PCNM: A New Platform for Cellular Networks Measurements and Optimization

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