<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper accurate estimation of parameters, higher order state space prediction methods and Extended Kalman filter (EKF) for modeling shadow power in wireless mobile communications are developed. Path-loss parameter estimation models are compared and evaluated. Shadow power estimation methods in wireless cellular communications are very important for use in power control of mobile device and base station. The methods are validated and compared to existing methods, Kalman Filter (KF) with Gaussian and Non-Gaussian noise environments. These methods provide better parameter estimation and are more accurate in most realistic situations. EKF can estimate the model channel parameters and predict states in state-space.</p></abstract>

In this paper accurate estimation of parameters, higher order state space prediction methods and Extended Kalman filter (EKF) for modeling shadow power in wireless mobile communications are developed. Path-loss parameter estimation models are compared and evaluated. Shadow power estimation methods in wireless cellular communications are very important for use in power control of mobile device and base station. The methods are validated and compared to existing methods, Kalman Filter (KF) with Gaussian and Non-Gaussian noise environments. These methods provide better parameter estimation and are more accurate in most realistic situations. EKF can estimate the model channel parameters and predict states in state-space.

Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper accurate estimation of parameters, higher order state space prediction methods and Extended Kalman filter (EKF) for modeling shadow power in...

Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

Online veröffentlicht: 27. Dez. 2017

Seitenbereich: 898 - 924

Eingereicht: 31. Jan. 2014

Akzeptiert: 01. Juni 2014

DOI: https://doi.org/10.21307/ijssis-2017-687

Schlüsselwörter
Extended Kalman Filter, Fading Channel, Handoff, Kalman Filter, local mean, multipath, power estimation, shadowing, state space, Path-Loss, Parameter Estimation

© 2014 George P. Pappas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

Online veröffentlicht: 27. Dez. 2017

Seitenbereich: 898 - 924

Eingereicht: 31. Jan. 2014

Akzeptiert: 01. Juni 2014

DOI: https://doi.org/10.21307/ijssis-2017-687

SchlüsselwörterExtended Kalman Filter, Fading Channel, Handoff, Kalman Filter, local mean, multipath, power estimation, shadowing, state space, Path-Loss, Parameter Estimation

© 2014 George P. Pappas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Extended Kalman Filter, Fading Channel, Handoff, Kalman Filter, local mean, multipath, power estimation, shadowing, state space, Path-Loss, Parameter Estimation