On the Design of Effective New Radio Sounding Reference Signal-Based Channel Estimation: Linear Regression with Channel Impulse Response Refinement

In this paper, we introduce a robust framework for linear regression–based channel estimation (CE) designed for multipath channel environments within a new radio (NR) sounding reference signal (SRS) system. The main contribution of this study is to show that integrating channel impulse response (CIR) refinement with existing CE schemes significantly improves CE performance in terms of normalized mean squared error (NMSE). Specifically, our approach employs thresholding-based CIR refinement to eliminate noise tap components effectively, discern the lengths of dominant tap elements, and augment linear regression–based CE’s efficacy. Specifically, it is shown that increasing the number of channel taps for threshold setting further enhances the performance of regression–based CE by leveraging CIR refinement. By utilizing an optimized threshold design, our results reveal close performance compared to both ideal tap information-based regression and the theoretical performance of linear minimum mean square error (LMMSE) estimation, whose findings are substantiated by numerical analyses employing our proposed polynomial regression–based channel estimation (PRCE) and DFT regression–based channel estimation (DRCE) schemes.