A physical mobile radio channel model

Braun, W. R.; Dersch, U.

doi:10.1109/25.289429

Cited by 420 publications

(178 citation statements)

References 5 publications

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“…This work is different from the published work which assumes either the RP or the BW channels are error-free [5], [6] or suffered from Rayleigh fading [7]. In this work, the fading channels are characterised by Nakagami-m distribution, which is used for modelling landmobile and indoor-mobile multipath propagation [8], [9].…”

Section: Introductionmentioning

confidence: 87%

Performance analysis of cooperative spectrum sensing for cognitive wireless radio networks over Nakagami-m fading channels

Vien

Nguyen

Trestian

et al. 2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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Abstract-This paper is concerned with cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical scenario is investigated where all channels suffer from Nakagami-m fading. Specifically, we analyse the probabilities of missed detection and false alarm for two CSS schemes where the collaboration is carried out either at fusion centre (FC) only or at both the FC and secondary user (SU). By deriving closed-form expressions and bounds of these probabilities, we not only show that there are significant impacts of the m-parameter of Nakagami fading realisation for different channel links on the sensing performance but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. Finally, numerical results are provided to validate the theoretical analysis and findings.

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Section: Introductionmentioning

confidence: 87%

Performance analysis of cooperative spectrum sensing for cognitive wireless radio networks over Nakagami-m fading channels

Vien

Nguyen

Trestian

et al. 2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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“…Experimentally, Howard et al have demonstrated that signal modeling yields worse localization results than signal mapping [17]. Electromagnetic signals are distorted due to "typical wave phenomena like diffraction, scattering, reflection, and absorption" [4]. These challenges account for the limited availability of practical implementations of algorithms based on this paradigm.…”

Section: Related Workmentioning

confidence: 99%

Real-time WiFi localization of heterogeneous robot teams using an online random forest

2015

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In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest. The algorithm's robustness is increased by a Monte Carlo Localization algorithm whose sensor model exploits the results of the online random forest classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points' location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.

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“…The frameworks for modeling and simu lating the satellite channel along with the time variations have been extensively reported in the literature [14][15][16]. It is widely accepted that, while the lognormal-Ray leigh model accurately depicts the shadowed channel, a Rician channel model is mo re appropriate for the unshadowed channel [9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation

Kumar

Gupta²,

Singh³

et al. 2013

IJISA

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Abstract-An effort has been made to illustrate the performance comparison of the Rayleigh and Rician fading channel models by using MATLAB simu lation in terms of source velocity and outage probability. We have developed algorith ms for the Ray leigh and Rician fading channels, which co mputes the envelop and outage probability. The parameters such as source velocity and outage probability play very important ro le in the performance analysis and design of the digital communicat ion systems over the multipath fading environment.

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A physical mobile radio channel model

Cited by 420 publications

References 5 publications

Performance analysis of cooperative spectrum sensing for cognitive wireless radio networks over Nakagami-m fading channels

Performance analysis of cooperative spectrum sensing for cognitive wireless radio networks over Nakagami-m fading channels

Real-time WiFi localization of heterogeneous robot teams using an online random forest

Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation

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