Deep Reinforcement Learning Based Bandwidth Part Assignment in 5G NR-U

In order to enhance 5G capability, 3GPP proposed the expansion of NR operation into the unlicensed spectrum (NR-U). However, channel access in the unlicensed band is challenging due to other coexisting technologies such as WiFi and LTE Licence Assisted Access (LAA). Unlicensed channel access protocols like Listen Before Talk (LBT) can degrade the NR performance compared to the licensed case. Prior work has shown that using Bandwidth Parts (BWP) in NR-U can help alleviate the situation. The concept of BWP was introduced in 5G in order to flexibly provide differentiated services based on varying requirements. However, dynamically deciding to which BWP a UE should be assigned still remains a challenge. In this work, we propose two solutions to effectively assign BWPs in order to decrease the Head of Line (HoL) delay and increase the throughput for NR-U. The first approach, Least Collision Assignment (LCA), uses a provably optimal heuristic under certain conditions. The second one uses a Deep Recurrent Q-Network (DRQN) based reinforcement learning model for assignment (RLA). Our results show significant improvements in delay and throughput of 60.27% and 70.88% with LCA, and 36.37% and 19.42% with RLA.