Name
Abstract | ||
|---|---|---|
In this paper, we consider a multiuser impulse radio ultra-wideband (UWB) system and focus on
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a single target user</italic>
's signal reception in the presence of multiuser interference (MUI) and/or inter-symbol interference (ISI). We propose a deep learning (DL) based receiver, which is trained off-line by simulated data to learn the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint effect</italic>
of UWB pulsing, channel effect, and signal detection. Then we apply it online to recover the real-time transmitted data symbols. Numerical results demonstrate that the deep-learning based receiver can efficiently learn such
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint effect</italic>
in the presence of MUI and ISI, leading to much better bit error rate (BER) performance than conventional correlation receiver and other existing receivers. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVT.2020.2972510 | IEEE Transactions on Vehicular Technology |
Keywords | DocType | Volume |
UWB,receiver,deep learning,neural network,multiuser interference,and inter-symbol interference | Journal | 69 |
Issue | ISSN | Citations |
3 | 0018-9545 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanjeev Sharma | 1 | 236 | 30.20 |
| Yi Hong | 2 | 621 | 45.07 |