Cellular Radio Network Planning using Particle Swarm Optimization

Elkamchouchi, Hassan M.; Elragal, Hassan M.; Makar, Mina

doi:10.1109/nrsc.2007.371389

Cited by 18 publications

(12 citation statements)

References 2 publications

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“…Over the past years, many different tools based on PSO have been proposed for wireless network planning. In [22,23], a PSO algorithm was developed to plan wavelength-division-multiplexed networks, cellular radio networks, and RFID networks, respectively. In order to obtain better results, [24] proposed an altered version of the PSO algorithm to solve the network planning problem in RFID systems.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

et al. 2014

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Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

et al. 2014

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“…Once the approximate number of BSs is known from the dimensioning phase, several planning techniques can be employed, e.g. based on genetic algorithms [19], particle swarm optimization [20], tabu search [21], and others [22], [23]. For example, the number of BSs obtained from the dimensioning phase can de distributed uniformly over a given area with uniform user distribution.…”

Section: From Dimensioning To Detailed Planning and Deploymentmentioning

confidence: 99%

A generic simulation-based dimensioning approach for planning heterogeneous LTE cellular networks

Yaacoub

Imran

Dawy

2014

MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference

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This paper presents a generic approach for dimensioning LTE/LTE-A cellular networks. Dimensioning is the first step of cellular network planning that determines the approximate number of base stations (BSs) needed to cover a certain area. The proposed approach takes into account user density, service subscriptions, resource allocation, and interference mitigation. It can be implemented in heterogeneous networks involving macrocells, microcells, distributed antenna systems, relays, femtocells, etc. The proposed dimensioning approach determines the number of BSs based on extensive simulations, and allows to analyze the impact of small cells in offloading macrocell traffic.

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“…Vasquez et al proposed a Tabu-based heuristic approach for antenna positioning [21] using the quintuplet BS compound (site, antenna, tilt, azimuth, and power). Elkamchouchi et al [22] developed work based on a particle swarm optimization approach and included morphological data in their internal representation matrix.…”

Section: A Rnd Research Foundationsmentioning

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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Cellular Radio Network Planning using Particle Swarm Optimization

Cited by 18 publications

References 2 publications

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

A generic simulation-based dimensioning approach for planning heterogeneous LTE cellular networks

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

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