Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels
arxiv(2024)
Abstract
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.
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