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Channel Estimation and Beamforming Using Constrained q-Renyi Kernel Functioned Adaptive Algorithm
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AI
DOI：10.1109/TCSII.2022.3145574.png)
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Abstract
En 中文
This brief proposed a constrained q-Renyi kernel functioned (CqRKF) adaptive algorithm, which used new cost function created by q-Renyi kernel function within the solution of constrained adaptive algorithms. The proposed CqRKF is implemented via solving a constrained optimization problem to provide superior performance against non-Gaussian noises. The theoretical analysis of steady-state mean square deviation (MSD) of proposed CqRKF has been presented and investigated under Gaussian and non-Gaussian noises. Computer simulations give a confirmation of the good match between the theoretical and the simulated values. For the sake of comparison, we carried out several computer simulations for beamforming and channel estimation, and simulation results shown that the CqRKF provides superior performance than the other conventional methods in non-Gaussian environment.
Keywords:
Channel estimation
constrained filtering algorithm
non-Gaussian noise
q-Renyi kernel function
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