# Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels

arXiv: https://arxiv.org/abs/2408.04182 · HTML: https://mlsanity.com/paper/2408.04182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Novel Deep Neural OFDM Receiver Architectures for LLR Estimation](https://mlsanity.com/paper/2503.20500/) (2025)
- [Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers](https://mlsanity.com/paper/2505.15570/) (2025)
- [Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS](https://mlsanity.com/paper/2602.20361/) (2026)

## Abstract (arXiv)

Recent research shows that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, most AI-based receiver studies rely on simulated radio channel data for both training and validation, raising concerns about real-world generalization, which is vital for ensuring reliable field performance. In this study, we train DeepRx, a convolutional neural network (CNN)-based OFDM receiver, under various simulated channel scenarios and validate its performance over-the-air (OTA) using software-defined radio (SDR) technology in a small cell-type setup. To enhance receiver training, we investigate a randomized 3GPP TS38.901 channel model to diversify the training data, thereby improving performance over conventional receivers and matching or exceeding the performance of receivers trained on narrowly targeted channel models. These results demonstrate DeepRx's robust generalization capability and suggest that narrowly scoped, individual TS38.901 models can compromise both training and validation, underscoring the need for tailored channel models, careful training strategies, and OTA testing in learned receiver development.