# AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results

arXiv: https://arxiv.org/abs/1812.06638 · HTML: https://mlsanity.com/paper/1812.06638/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer](https://mlsanity.com/paper/2211.09712/) (2022)
- [Learn to Adapt to New Environment from Past Experience and Few Pilot](https://mlsanity.com/paper/2209.02649/) (2022)
- [Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS](https://mlsanity.com/paper/2602.20361/) (2026)

## Abstract (arXiv)

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.