DRL-based Channel Access in NR Unlicensed Spectrum for Downlink URLLC

To improve the capacity of cellular systems without additional expenses on licensed frequency bands, the 3rd Gen-eration Partnership Project (3GPP) has proposed New Radio Unlicensed (NR-U). It should be noted that each node in NR-U has to perform the Listen-Before- Talk (LBT) operation before transmission to avoid collisions by other unlicensed radio access technologies (e.g., WiFi). Thus, packets transmissions are prone to delay due to the LBT channel access mechanism. How to achieve Ultra-Reliable and Low-Latency Communications (URLLC) requirements in NR-U networks under the coexistence with WiFi networks is of importance and extremely challenging. In this paper, we develop a novel deep reinforcement learning (DRL) framework to optimize the downlink URLLC trans-mission in the NR-U and WiFi coexistence system through dynamically adjusting the energy detection (ED) thresholds. Our results have shown that the NR-U system reliability has been improved significantly via the DRL compared to that without learning approaches, but with the sacrifice of WiFi system reliability. To address this, we redesigned the reward to take fairness into account, which guarantees the WiFi system reliability while improvina the NR- U system reliability.