A Genetic Algorithm Approach to User Location Estimation in UMTS Networks

Magro, Mark; Debono, Carl James

doi:10.1109/eurcon.2007.4400320

Cited by 14 publications

(15 citation statements)

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“…Mahdi Orooji and Bahman Abolhassani [3] proposed a new algorithm to detect the location of the mobile phone which is based on the received signal strength. In [4], a solution to detect the mobile location in 3G networks was proposed. In [5] the author proposed a method to correct the feasibility of applying database to locate mobile station within a live GSM cellular network.…”

Section: Past Approchesmentioning

confidence: 99%

Three dimensional location detection using received signal strength

Das

2013

2013 International Conference on Signal Processing , Image Processing &Amp; Pattern Recognition

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In this paper an algorithm has been proposed based on Hata model that employs an improved procedure in detecting the mobile location in three dimensional environment using received signal strength (RSS). This technique considers path loss and shadowing effect for the channel to compute the distance between the mobile phone and its home base station and neighboring base stations using Hata model. Considering the distance from the mobile phone to the base station (including its home base station) as the radius of the sphere (i.e., shape produced by signal propagation from Omni directional antenna) to find out an intersection point of all these spheres that denotes the location of the mobile phone. An intersection point of all these spheres is computed that denotes the location of the mobile phone. The algorithm has been simulated and the result has been found to be accurate.

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Section: Past Approchesmentioning

confidence: 99%

Three dimensional location detection using received signal strength

Das

2013

2013 International Conference on Signal Processing , Image Processing &Amp; Pattern Recognition

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“…In [13] GA was used to reduce the correlation space in RF fingerprinting location method to improve location accuracy and authors have claimed their method to be suitable for UE positioning in urban environments. Authors in [14] have proposed a location detection algorithm which employs cell-id positioning enhanced by triangulation. The process was accelerated through the application of a GA.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks

Mondal

Ristaniemi

Turkka

2015

2015 International Conference on Location and GNSS (ICL-GNSS)

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Abstract-In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of gridcell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional gridbased RF fingerprinting.

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“…However, we propose an alternative for the population initialization, restricting the random distribution of candidate solutions in the first generation to the predicted best server area of the serving cell (also referred to as sector). The proposed innovation reduced both the average location error and the average time to produce a position fix, in comparison to the original formulation presented in [7].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, this is done by a genetic algorithm (GA). GA has already been used together with DCM solutions in [7]. However, we propose an alternative for the population initialization, restricting the random distribution of candidate solutions in the first generation to the predicted best server area of the serving cell (also referred to as sector).…”

Section: Introductionmentioning

confidence: 99%

Providing Higher Entropy Cryptographic Keys by the Use of Biometrics

Camara¹

2010

Anais De VII International Telecommunications Symposium

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Radio Frequency Fingerprinting estimates the mobile station location by comparing a measured radio frequency fingerprint with a set of previously collected or generated reference fingerprints. This set is referred to as the search or correlation space. Genetic algorithms can be used to optimize both the location accuracy and the time required to produce a position fix, reducing the size of the search space. This paper proposes an innovation in such application of genetic algorithms, restricting the first generation population to the predicted best server area of the serving sector measured by the mobile station. In field tests in a GSM cellular network in a dense urban environment, this approach achieved reductions of 20% and 15% in the 50-th and 90-th percentile location errors, respectively, in comparison to the original formulation, where the initial population is randomly distributed throughout the whole service area. An average reduction of 91% in the time to produce a position fix was also observed.

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A Genetic Algorithm Approach to User Location Estimation in UMTS Networks

Cited by 14 publications

References 1 publication

Three dimensional location detection using received signal strength

Three dimensional location detection using received signal strength

Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks

Providing Higher Entropy Cryptographic Keys by the Use of Biometrics

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