Optimisation models for GSM radio

Mathar, Rudolf; Schmeink, Michael

doi:10.1504/ijmndi.2005.007937

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…When x tβ = 1 then (4) reduces to (2); when instead x tβ = 0 and M is sufficiently large (for example, we can set M = −δ + b∈B\{β} a tb • P max ), ( 4) is satisfied for any feasible power vector and becomes redundant. Constraints of type (4) appear in the Mixed-Integer Linear Programs (MILP) for the WND presented in several papers in different application contexts, such as radio and video broadcasting (e.g., Mannino et al 2006Mannino et al , 2009, GSM (e.g., Mathar and Schmeinck 2005), UMTS (e.g., Amaldi et al 2006a, Eisenblätter and Geerdes 2008, Kalvenes et al 2006, Naoum-Sawaya and Elhedhli 2010, WiMAX (Zhang 2009). Such MILPs are informally called big-M formulations.…”

Section: Article Published In Management Sciencementioning

confidence: 99%

GUB Covers and Power-Indexed Formulations for Wireless Network Design

2013

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W e propose a pure 0-1 formulation for the wireless network design problem, i.e., the problem of configuring a set of transmitters to provide service coverage to a set of receivers. In contrast with classical mixed-integer formulations, where power emissions are represented by continuous variables, we consider only a finite set of power values. This has two major advantages: it better fits the usual practice and eliminates the sources of numerical problems that heavily affect continuous models. A crucial ingredient of our approach is an effective basic formulation for the single knapsack problem representing the coverage condition of a receiver. This formulation is based on the generalized upper bound (GUB) cover inequalities introduced by Wolsey [Wolsey L (1990) Valid inequalities for 0-1 knapsacks and mips with generalised upper bound constraints. Discrete Appl. Math. 29(2-3):251-261]; and its core is an extension of the exact formulation of the GUB knapsack polytope with two GUB constraints. This special case corresponds to the very common practical situation where only one major interferer is present. We assess the effectiveness of our formulation by comprehensive computational results over realistic instances of two typical technologies, namely, WiMAX and DVB-T

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Section: Article Published In Management Sciencementioning

confidence: 99%

GUB Covers and Power-Indexed Formulations for Wireless Network Design

2013

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“…After the appearance of the TP model, more accurate models and effective optimization algorithms have emerged for the cell placement problem, finding suitable site positions and base station parameters for the coverage of a wireless network [27]. A number of models defined by associating suitable decision variables with the physical and radioelectrical antenna parameters (candidate locations, power values, activation statuses, transmission frequencies, service, and coverage requirements ) and solving coverage and capacity planning problems have been developed and applied to the design of Global System for Mobile Communications (GSM) [106,140,83,143,139,22,144] and Universal Mobile Telecommunications System (UMTS) [26,51,25,142,137,141,24]. Authors in [138] have provided a detailed survey of proposed approaches for network design, charting the evolution of models and techniques for the automatic planning of cellular wireless services.…”

Section: Rf Optimizationmentioning

confidence: 99%

Optimized network dimensioning and planning for WiMAX technology

Teterin

Hurley

2014

Ad Hoc Networks

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for their help and encouragement. I would also like to say that I was very lucky to be supervised by Prof. Steve Hurley as he is a very special person, he has been my inspiration over the course of this work, and I would like to thank him for his great patience, enormous support in all aspects, for having faith in me and keeping me positive all the way. I would like to thank the best Mum in the world, who made this all possible and gave me wings when I needed it most.

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“…Although some works related to RND applied research are found (namely 2G and 3G), few demonstrate optimization techniques, preferring to confine themselves to the proposal of planning methods or procedures [37], [38]. A hierarchical parallel approach for GSM network design has been proposed by Talbi et al [39].…”

Section: Rnd Applied Researchmentioning

confidence: 99%

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

Priem-Mendes

Molina

Vega‐Rodríguez

et al. 2009

IEEE Trans. Evol. Computat.

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Abstract-The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

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Optimisation models for GSM radio

Cited by 7 publications

References 14 publications

GUB Covers and Power-Indexed Formulations for Wireless Network Design

GUB Covers and Power-Indexed Formulations for Wireless Network Design

Optimized network dimensioning and planning for WiMAX technology

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

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