2007/07/02 by Xinjia Chen, Chen, Xinjia, Guoxiang Gu +3
Computer Science · Engineering · Mathematics · #Advanced Wireless Communication Techniques #Algorithm #Applications (stat.AP) #Bias of an estimator #Channel (broadcasting) #Channel state information #Computer science #Cramér–Rao bound #Direction-of-Arrival Estimation Techniques #Estimator #FOS: Computer and information sciences #FOS: Mathematics #Fading #Fading distribution #Mathematics #Minimum-variance unbiased estimator #Physics #Probability (math.PR) #Rayleigh fading #Rayleigh scattering #Statistic #Statistics #Statistics Theory (math.ST) #Telecommunications #Wireless #Wireless Communication Networks Research #math.PR #math.ST #stat.AP #stat.TH
paper · pdf · doi:10.48550/arxiv.0707.0284
published in arXiv (Cornell University) (Cornell University) · 25 pages, 10 figures
arxiv created 2007/07/02 · openalex publication_date 2007/07/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this paper, we propose a statistical theory on measurement and estimation of Rayleigh fading channels in wireless communications and provide complete solutions to the fundamental problems: What is the optimum estimator for the statistical parameters associated with the Rayleigh fading channel, and how many measurements are sufficient to estimate these parameters with the prescribed margin of error and confidence level? Our proposed statistical theory suggests that two testing signals of different strength be used. The maximum likelihood (ML) estimator is obtained for estimation of the statistical parameters of the Rayleigh fading channel that is both sufficient and complete statistic. Moreover, the ML estimator is the minimum variance (MV) estimator that in fact achieves the Cramer-Rao lower bound.