AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2018
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916457521086464 |
|---|---|
| author | Jiang, Peiwen Wang, Tianqi Han, Bin Gao, Xuanxuan Zhang, Jing Wen, Chao-Kai Jin, Shi Li, Geoffrey Ye |
| author_facet | Jiang, Peiwen Wang, Tianqi Han, Bin Gao, Xuanxuan Zhang, Jing Wen, Chao-Kai Jin, Shi Li, Geoffrey Ye |
| contents | Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems. We discuss potential challenges and future research inspired by our initial study in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1812_06638 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results Jiang, Peiwen Wang, Tianqi Han, Bin Gao, Xuanxuan Zhang, Jing Wen, Chao-Kai Jin, Shi Li, Geoffrey Ye Signal Processing Information Theory Machine Learning Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems. We discuss potential challenges and future research inspired by our initial study in this paper. |
| title | AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/1812.06638 |