A hybrid HARQ feedback prediction approach for Single- and Cloud-RANs in the sub-THz regime

In this work, we extend two autoencoder-based HARQ prediction schemes to exploit subcode-based features and SNR-based features jointly. We apply the proposed HARQ prediction schemes to Cloud-RAN (C-RAN) and Single-RAN (S-RAN) architectures. Furthermore, we conduct realistic link-level simulations to test the performance and compare to state-of-the-art prediction schemes that rely solely on either subcode-based features or SNR-based features. Compared to the state-of-the-art, we show that the proposed schemes reduce the transmitted redundancy at a target error rate of · 10-5 and 2· 10-5 by 12.3% - 27.3% in C-RAN architectures and 10.5% - 11.0% in S- RAN architectures, respectively.