AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
arXiv:1812.06638 · doi:10.1109/TWC.2021.3087191
Abstract
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.
arXiv comment30 pages, 12 figures
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Cited by in corpus (4)
- Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems
- Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures
- Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots
- Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers