RaP: Learning-based Joint Reservation and Puncturing for Efficient URLLC/eMBB Multiplexing

Ultra-Reliable Low-Latency Communication (URLLC) is an important communication service in 5G NR, designed to support extremely delay-sensitive and ultra-reliable applications. To multiplex URLLC and Enhanced Mobile Broadband (eMBB) on the same air interface, two approaches have been proposed, namely, puncturing and reservation. While puncturing can guarantee timely URLLC delivery, it degrades eMBB goodput. On the other hand, reservation may lead to under-utilization when URLLC packet arrival becomes bursty. In this paper, we propose RaP—a novel scheduling solution that intelligently integrates both reservation and puncturing. RaP exploits the small window of URLLC’s delay tolerance by employing buffering/reservation based on a Long Short-Term Memory (LSTM)-based predictor, while performing puncturing using a deep reinforcement learning (DRL) agent when actual URLLC arrival exceeds prediction. We implement RaP with a link-level 5G NR simulator and show that RaP significantly outperforms state-of-the-art schemes and achieve a comparable performance with a ideal reservation scheme assuming perfect knowledge of URLLC packet arrival.