Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network

TL;DR

This paper introduces a neural network-based method for estimating the Ricean K factor at the transmitter side in OFDM systems, improving accuracy and reducing feedback bandwidth compared to traditional receiver-based methods.

Contribution

It presents a novel neural network approach to estimate the Ricean K factor at the transmitter side, unlike prior methods that focus on receiver-side estimation.

Findings

High accuracy in K-factor estimation using neural networks

Estimation performed at transmitter side reduces feedback bandwidth

Outperforms traditional methods based on likelihood and moments

Abstract

Ricean channel model is widely used in wireless communications to characterize the channels with a line-of-sight path. The Ricean K factor, defined as the ratio of direct path and scattered paths, provides a good indication of the link quality. Most existing works estimate K factor based on either maximum-likelihood criterion or higher-order moments, and the existing works are targeted at K-factor estimation at receiver side. In this work, a novel approach is proposed. Cast as a classification problem, the estimation of K factor by neural network provides high accuracy. Moreover, the proposed K-factor estimation is done at transmitter side for transmit processing, thus saving the limited feedback bandwidth.