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

## TL;DR

This paper compares AI-aided OFDM receivers through simulations and real-world tests, introduces SwitchNet for online adaptation to real channels, and demonstrates its robustness and potential for future communication systems.

## Contribution

It presents a novel online training system, SwitchNet, enabling AI-based OFDM receivers to adapt to real channels with minimal online training.

## Key findings

- SwitchNet outperforms traditional AI receivers in real OTA tests.
- AI receivers show robustness and promise for future communication systems.
- Discrepancy between simulation and real environment affects performance.

## 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.