Fast DOA estimation using wavelet denoising on MIMO fading channel

TL;DR

This paper introduces a wavelet denoising pre-processing step for DOA estimation in MIMO fading channels, enhancing accuracy at low SNR and interference, and compares MUSIC and Cyclic MUSIC methods.

Contribution

It proposes a wavelet denoising approach for improved DOA estimation and analyzes its performance with MUSIC and Cyclic MUSIC algorithms in fading channels.

Findings

Wavelet denoising improves DOA estimation accuracy at low SNR.

Cyclic MUSIC effectively classifies signals and estimates DOA in interference.

Performance analysis shows the proposed method enhances statistical efficiency.

Abstract

This paper presents a tool for the analysis, and simulation of direction-of-arrival (DOA) estimation in wireless mobile communication systems over the fading channel. It reviews two methods of Direction of arrival (DOA) estimation algorithm. The standard Multiple Signal Classification (MUSIC) can be obtained from the subspace based methods. In improved MUSIC procedure called Cyclic MUSIC, it can automatically classify the signals as desired and undesired based on the known spectral correlation property and estimate only the desired signal's DOA. In this paper, the DOA estimation algorithm using the de-noising pre-processing based on time-frequency conversion analysis was proposed, and the performances were analyzed. This is focused on the improvement of DOA estimation at a lower SNR and interference environment. This paper provides a fairly complete image of the performance and statistical efficiency of each of above two methods with QPSK signal.