Cross-Site Transfer and Spatial Forgetting in OTA-Trained Neural Receivers

Abstract

Most published neural-receiver studies train on synthetic channel models, although real over-the-air measurements can improve performance. We examine how much of this benefit transfers from other sites to a distinctly different deployment site and what adaptation to that site costs elsewhere. The study uses a dataset from a 5.88 GHz measurement campaign covering eight sites and four environment types. An industrial site measured entirely from a vehicle is withheld as a deliberate transfer stress test. One receiver architecture is trained under four regimes: simulation alone; seven other sites; all sites jointly; and other-site training followed by deployment-site finetuning. The last two use identical measurements through different optimization protocols. Across five independently trained seeds, sensitivity gains are evaluated at uncoded bit error rate (BER) targets of $10^{-1}$ and $10^{-2}$, representative of moderate- and higher-rate coded links, respectively. On the deployment site, training with other-site data rather than simulation improves sensitivity by 0.3 dB at $10^{-1}$ and 0.1 dB at $10^{-2}$. Adding deployment-site data contributes less than 0.1 dB at $10^{-1}$; at $10^{-2}$, total gains over simulation reach 0.4 dB with joint training and 0.6 dB with finetuning. Joint training shows no measurable aggregate loss on the other-site test set, whereas finetuning yields the larger deployment-site gain but reduces other-site sensitivity by about 0.1 dB at $10^{-2}$, indicating spatial forgetting.