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Fading channel prediction based on complex-valued neural networks in frequency domain
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ding, Tianben Hirose, Akira |
| Copyright Year | 2013 |
| Abstract | Channel prediction is an important process for channel compensation in fading environment. If a future channel state is predicted, adaptive techniques such as pre-equalization and transmission power control is applicable before transmission in order to avoid degradation of communication quality. Previously, we proposed frequency-domain channel prediction methods employing the chirp z-transform with a linear extrapolation as well as a Lagrange-based nonlinear extrapolation of frequency-domain parameters. This paper presents a highly accurate method for predicting time-varying channels by using a complex-valued neural-network (CVNN) based prediction of frequency-domain channel characteristics. We demonstrate that the channel prediction accuracy of our CVNN method is better than those of the previous linear- and Lagrange-extrapolation prediction in a series of simulations for orthogonal frequency division multiplexing (OFDM) communications. |
| Starting Page | 640 |
| Ending Page | 643 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ieice.org/proceedings/EMTS2013/program/23PM2E_01.pdf |
| Journal | 2013 International Symposium on Electromagnetic Theory |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
