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Thus, in a multicell environment to fully exploit the multiple antenna gain, base station (BS) cooperation is required.
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Consequently, the Kalman-filter-based data fusion optimally adjusts the fusion gain based on environment conditions derived from each local estimator.
The neighborhood follow strategy considers relative mobility, gain based on the number of followers, and gain based on the historical cluster belonging information.
where g1,k(n) is the subband gain based on estimation of the SNR in subband k.
A design method of the state space feedback gain based on the dynamics, and an observer gain based on basic Kalman filter theory is given.
The filter is designed to automatically tune its gain based on the dynamic system state.
The proposed approach uses a high gain based controller in order to achieve the control objective.
A state feedback gain based controller is designed for each critical region.
We present a workflow for the optimization of screening gain based on conformal prediction and a gain-cost function.
In this algorithm, firstly, channel prediction algorithm is used to predict the current channel gain based on previous channel gains.
By calculating the performance gain based on Equation 9, (((7.76−13.30)/13.30)×100), the improved percentage in this scenario is 41.65%.
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