Approximate Maximum Likelihood Estimation of Rician K-Factor and Investigation of Urban Wireless Measurements

• Published in: IEEE transactions on wireless communications Vol. 12; no. 6; pp. 2545 - 2555
• Main Authors: Medawar, S., Handel, P., Zetterberg, P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-06-2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; approximate pdf; Approximation; Approximation methods; bias; Channels; Density; Estimators; Exact sciences and technology; Exact solutions; K-factor; Mathematical analysis; Mathematical model; Maximum likelihood estimation; Monte Carlo methods; Portable document format; Rician channels; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Tin; Transmission and modulation (techniques and equipments); variance; wireless channel measurements; Wireless communication; Fading channels; Performance evaluation; Wireless telecommunication; Rice fading; Urban area; maximum likelihood estimation; wireless channel measurements; K-factor; variance; approximate pdf; bias; Telecommunication channels; Density function; Maximum likelihood; Low k dielectric
• ISSN: 1536-1276; 1558-2248; 1558-2248
• DOI: 10.1109/TWC.2013.042413.111734

We consider the problem of estimating the K-factor of a Rician fading wireless channel based on observations of the envelope only, i.e., without phase-information. An approximate Rician power density function (pdf) is introduced to overcome the complexity of the exact pdf. A closed-form maximum-likelihood estimator is derived based on this approximate Rician pdf, while a maximum-likelihood estimator based on the exact Rician pdf appears to be infeasible. An improved estimator is also proposed that features less bias and less variance than the first estimator. The performance of the latter estimator is compared to moment-based estimators that were previously proposed in the literature and is found to have superior performance to closed-form moment estimators, especially for low sample numbers and/or large K-values. The estimator is applied to real wireless macro-cell urban-area measurements. The results show generally low K-factors (below 3dB), with occasional higher values in particular circumstances.