Xavier-Enabled Extreme Reservoir Machine for Millimeter-Wave Beamspace Channel Tracking

TL;DR

This paper introduces a Xavier-initialized extreme reservoir machine for millimeter-wave channel tracking, significantly improving spectral efficiency and reducing prediction variance through ensemble learning techniques.

Contribution

It proposes a novel two-phase mmWave channel tracking method using ERM with Xavier initialization and ensemble learning, achieving notable performance improvements.

Findings

Spectral efficiency increased by 13% at 15dB SNR.

Prediction variance reduced by 49% with Xavier initialization.

Ensemble learning further reduces variance by 56% and boosts SE by 21%.

Abstract

In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing counterparts, by 13%, when signal-to-noise-ratio (SNR) is 15dB. We further investigate an ensemble learning technique in the second phase by sequentially incorporating multiple ERMs to form an ensembled model, namely adaptive boosting (AdaBoost), which further reduces the prediction variance in conventional ERM by 56%, and concludes in 21% enhancement of achievable SE upon the existing schemes at SNR=15dB.