Name
Title | ||
|---|---|---|
Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network. |
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Signal Processing | Transmitter,Channel models,Wireless,Computer science,K factor,Communication channel,Electronic engineering,Bandwidth (signal processing),Artificial neural network,Orthogonal frequency-division multiplexing |
DocType | Volume | Citations |
Journal | abs/1808.06537 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |