Abstract
When the new thing, internet, formally entered into the commercial operation in 1990s, few people could think that the internet, which came from military technology, would drastically change the modes of obtaining information and communication by unheard-of speed. Undoubtedly, the developmental course less than 50 years from the advent of internet to the establishment of information society have made media theoreticians believe that internet was the most powerful media form beyond the memory of men, and it changed the way of traditional marketing, the medium and form of communication.There is medium, there is advertisement. The development of network advertisement form is continually evolves with the development of application mode of internet, and every sort of application mode will go with a sort of network advertisement form. Through the comparison and analysis of the development course of network advertisement, this article will analyze the development foreground of Web3.0 and its drive and support to the rich media advertisements.
Highlights
In mobile communication systems, local mean power estimation is very important and it must be an accurate estimate of the received signal power
The first part of this paper introduces the system model for the local mean power estimation using uncorrelated and correlated samples
The variance obtained with optimum unbiased estimator and maximum likelihood estimators are consistently lower than that obtained with sample average estimator
Summary
Local mean power estimation is very important and it must be an accurate estimate of the received signal power. Wong and Cox (1999) derived the optimal local mean signal level estimator for the Rayleigh fading environment and compared with the sample average estimator. Chai Ko and Mohamed-Slim Alouini presented maximum likelihood as well as minimum variance unbiased estimators for the local mean signal power estimation. The performance of the Kalman filter (KF) was compared with the window based estimators, like the sample average estimator of (Goldsmith, Greenstein & Foschini, 1994) the uniformly minimum variance unbiased (UMVU) estimator of (Wong & Cox, 1999) and the maximum likelihood (ML) estimator of (Tepedelenlioglu, Sidiropoulos and Giannakis, 2001). The performances of the sample average estimator, the optimum unbiased estimator and maximum likelihood estimator are studied with the use of uncorrelated and correlated samples (Rayleigh fading signal). The performances of the three local mean power estimators and conclusion are included in the subsequent sections
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
