Low-Complexity Full-Dimensional Wireless Channel Parameter Extraction Algorithm and Its Application in 5G-NR Systems

Precise parameter estimation is essential for developing effective channel models. However, jointly estimating the various channel parameters is a challenging problem as these parameters can only be extracted from comprehensive data collected during massive measurement activities. Traditional algorithms suffer from limited estimation accuracy due to high mobility and signal correlation. In this paper, we propose a low-complexity full-dimension parameter extraction algorithm. The method extends the forward-backward spatial smoothing technique to estimate two-dimensional (2-D) angle information in the uniform planar array (UPA), enhancing aperture size and providing higher resolution for coherent signals. Then, we provide a Doppler compensation technique to prevent the deterioration in complex amplitude estimation quality caused by large Doppler frequency in high-speed mobile environments. Moreover, the proposed algorithm is also applied to the fifth-generation new radio (5G-NR) system. To remedy power leakage in the channel impulse response (CIR), we propose an eigenvalue-assisted sampling point selection scheme. This scheme preserves sampling positions abundant in channel information to reduce computational complexity and eliminate most noise. Simulation and field tests show that the proposed algorithm achieves better performance with lower computational complexity compared to the existing algorithm.