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.
Cite
@article{arxiv.1808.06537,
title = {Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network},
author = {Kun Wang},
journal= {arXiv preprint arXiv:1808.06537},
year = {2018}
}