%0 Journal Article %1 Mudaliar_2015 %A Mudaliar, Anirudh %A Patel, Deepika %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Filter Kalman Local Power average estimation estimator likelihood maximum mean power sample shadowing %N 4 %P 2220--2224 %R 10.17762/ijritcc2321-8169.150499 %T Overview of Power Estimation Methods in Mobile Communications %U http://dx.doi.org/10.17762/ijritcc2321-8169.150499 %V 3 %X This is a survey paper. In this paper various methods used for power estimation in wireless communications have been discussed. Wireless communications has become an inseparable part of our life. Power consumption is one of the major factors that decide the communication system quality. Accurate power estimation has an important role for power control and handoff decisions in mobile communications. Window based weighed sample average power estimators are commonly used due to their simplicity. In practice, the performances of these estimators degrade severely when the estimators are used in the presence of correlated samples. In this paper performances of the local mean power estimators namely, sample average, optimum unbiased and maximum likelihood estimators and Kalman Filter are analyzed in the presence of correlated samples. The variance of the estimators is used as performance measures.

@article{Mudaliar_2015, abstract = {This is a survey paper. In this paper various methods used for power estimation in wireless communications have been discussed. Wireless communications has become an inseparable part of our life. Power consumption is one of the major factors that decide the communication system quality. Accurate power estimation has an important role for power control and handoff decisions in mobile communications. Window based weighed sample average power estimators are commonly used due to their simplicity. In practice, the performances of these estimators degrade severely when the estimators are used in the presence of correlated samples. In this paper performances of the local mean power estimators namely, sample average, optimum unbiased and maximum likelihood estimators and Kalman Filter are analyzed in the presence of correlated samples. The variance of the estimators is used as performance measures.}, added-at = {2015-08-26T09:56:16.000+0200}, author = {Mudaliar, Anirudh and Patel, Deepika}, biburl = {https://www.bibsonomy.org/bibtex/2d9d8021a9549456af2cc86774d8d01a8/ijritcc}, doi = {10.17762/ijritcc2321-8169.150499}, interhash = {507c738d303257e74db29407c21eceac}, intrahash = {d9d8021a9549456af2cc86774d8d01a8}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Filter Kalman Local Power average estimation estimator likelihood maximum mean power sample shadowing}, month = {april}, number = 4, pages = {2220--2224}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-26T09:56:16.000+0200}, title = {Overview of Power Estimation Methods in Mobile Communications}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150499}, volume = 3, year = 2015 }