Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network
This work addresses a domain-specific problem in wireless communications for improving link quality estimation, but it is incremental as it builds on existing methods by shifting estimation to the transmitter.
The paper tackled the problem of estimating the Ricean K-factor in OFDM systems by proposing a neural network-based classification approach, achieving high accuracy and enabling estimation at the transmitter side to save feedback bandwidth.
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.