A Novel Channel Identification Architecture for mmWave Systems Based on Eigen Features

TL;DR

This paper introduces a new channel identification architecture for mmWave systems using eigen features, significantly improving accuracy and reducing overhead, especially in LOS and NLOS environments.

Contribution

It proposes a novel eigen feature-based architecture for channel identification in mmWave systems, enhancing efficiency and robustness with minimal computational overhead.

Findings

Achieves 99.88% identification accuracy with perfect CSI

Tolerates noise up to SNR=16 dB with at least 95% accuracy

Reduces system overhead by approximately 90%

Abstract

Millimeter wave (mmWave) communication technique has been developed rapidly because of many advantages of high speed, large bandwidth, and ultra-low delay. However, mmWave communications systems suffer from fast fading and frequent blocking. Hence, the ideal communication environment for mmWave is line of sight (LOS) channel. To improve the efficiency and capacity of mmWave system, and to better build the Internet of Everything (IoE) service network, this paper focuses on the channel identification technique in line-of- sight (LOS) and non-LOS (NLOS) environments. Considering the limited computing ability of user equipments (UEs), this paper proposes a novel channel identification architecture based on eigen features, i.e. eigenmatrix and eigenvector (EMEV) of channel state information (CSI). Furthermore, this paper explores clustered delay line (CDL) channel identification with mmWave, which is defined by the 3rd generation partnership project (3GPP). Ther experimental results show that the EMEV based scheme can achieve identification accuracy of 99.88% assuming perfect CSI. In the robustness test, the maximum noise can be tolerated is SNR= 16 dB, with the threshold acc \geq 95%. What is more, the novel architecture based on EMEV feature will reduce the comprehensive overhead by about 90%.