Input-Correlated Supervision Noise Limits the Benefits of OTA Training for Learned Receivers

While learned wireless receivers are typically studied using synthetic data, the impact of over-the-air (OTA) measurements for training remains unclear. We conducted a 5.88 GHz measurement campaign with a 5G/6G-like orthogonal frequency-division multiplexing (OFDM) system across diverse environments and mobility conditions, and trained a neural channel estimator and a capacity-matched end-to-end neural receiver using mixtures of measured and synthetic data. Increasing the OTA fraction revealed a fundamental asymmetry: measured data consistently improved the end-to-end receiver, whereas the channel estimator peaked at an intermediate fraction and degraded with fully measured training. We showed that this difference arises from the supervision target: OTA channel labels are derived from noisy received signals and therefore contain supervision errors correlated with the receiver input, whereas decoded bits validated by a cyclic redundancy check (CRC) provide effectively error-free supervision. A controlled denoising experiment confirmed that this correlation, rather than limited data diversity, caused the degradation. These results provide practical guidance for training learned receivers with OTA data: end-to-end receivers benefit from fully measured training, whereas channel estimators benefit from moderate OTA fractions but require improved label quality, e.g. via denoising, to unlock further gains.

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

Recent research has shown that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, the prevalent use of simulated radio channel data for training and validating neural network-based radios raises concerns about their generalization capability to diverse real-world environments. To address this, we conducted empirical over-the-air (OTA) experiments using software-defined radio (SDR) technology to test the performance of an NN-based orthogonal frequency division multiplexing (OFDM) receiver in a real-world small cell scenario. Our assessment reveals that the performance of receivers trained on diverse 3GPP TS38.901 channel models and broad parameter ranges significantly surpasses conventional receivers in our testing environment, demonstrating strong generalization to a new environment. Conversely, setting simulation parameters to narrowly reflect the actual measurement environment led to suboptimal OTA performance, highlighting the crucial role of rich and randomized training data in improving the NN-based receiver's performance. While our empirical test results are promising, they also suggest that developing new channel models tailored for training these learned receivers would enhance their generalization capability and reduce training time. Our testing was limited to a relatively narrow environment, and we encourage further testing in more complex environments.