Nonlinear anisotropic diffusion-based channel estimation in 5G wireless networks

Abstract

In the context of the fifth-generation new radio downlink scenario, we introduce an innovative approach for channel estimation in this paper that circumvents the requirement for the prior dataset. We incorporate anisotropic diffusion and bit-plane decomposition to remove the noise in channel estimates. We first pre-process wireless channel coefficients with bit-plane decomposition to partially reduce noise interference and maintain the granularity of the information. In the second stage, anisotropic diffusion is performed based on neighboring coefficients, and the gradient-based denoising takes place without prior training. We assess the mean square error (MSE) across varying noise levels compared to the state-of-the-art method and further explore the impact of key parameters governing anisotropic diffusion. The simulation results indicate that the proposed channel estimation technique achieves a 44.77% reduction in average MSE and a significant reduction in computational complexity compared to the baseline reference technique.