摘要：Optimizing the estimates of received power signals is important as it can improve the process of transferring an active call from one base station in a cellular network to another base station without any interruptions to the call. The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering (KF) as an effective alternative. In our research, linear second-order state space Kalman Filtering was further investigated and tested for applicability. We first created simulation models for two KF-based estimators designed to estimate local mean (shadow) power in mobile communications corrupted by multipath noise. Simulations were used extensively in the initial stage of this research to validate the proposed method. The next challenge was to determine if the models would work with real data. Therefore, in [1] we presented a new technique to experimentally characterize the wireless small-scale fading channel taking into consideration real environmental conditions. The two-dimensional measurement technique enabled us to perform indoor experiments and collect real data. Measurements from these experiments were then used to validate simulation models for both estimators. Based on the indoor experiments, we presented new results in [2], where we concluded that the second-order KF-based estimator is more accurate in predicting local shadow power profiles than the first-order KF-based estimator, even in channels with imposed non-Gaussian measurement noise. In the present paper, we extend experiments to the outdoor environment to include higher speeds, larger distances, and distant large objects, such as tall buildings. Comparison was performed to see if the system is able to operate without a failure under a variety of conditions, which demonstrates model robustness and further investigates the effectiveness of this method in optimization of the received signals. Outdoor experimental results are provided. Findings demonstrate that the second-order Kalman filter outperforms the first-order Kalman filter.
