San, Thet Naung2024-11-042024-11-042024https://hdl.handle.net/1828/20713The mission critical applications such as industrial automation, remote surgery and autonomous transportation systems demand low-latency, high-reliability communications service. As such, there is an urgent need to optimize transmission technologies in 5G New Radio (NR) to support Ultra-Reliable Low-Latency Communication (URLLC). This project introduces a joint adaptive transmission and numerology selection scheme for Physical Downlink Shared Channel (PDSCH) in 5G NR, targeting URLLC support. The transmission scheme selection problem is modeled as a Markov Decision Process (MDP). A Deep Q-Network (DQN) reinforcement learning agent is trained to dynamically adjust Modulation and Coding Scheme (MCS) and numerology based on real-time channel conditions and latency constraints. To evaluate the performance, we develop custom simulation environment by implementing PDSCH transmission model under frequency-selective fading channels, incorporating the Hybrid Automatic Repeat reQuest (HARQ) mechanism. The results demonstrate that the DQN agent effectively reduces transmission delays and improves reliability by optimizing transmission parameters. This approach enhances performance for 5G NR in URLLC support, achieving both higher reliability and lower latency than conventional adaptive transmission system.en5G New Radio (NR)Ultra-Reliable Low-Latency Communication (URLLC)Physical Downlink Shared Channel (PDSCH)Deep Q-Network (DQN)reinforcement learningadaptive transmissionnumerology selectionModulation and Coding Scheme (MCS)Markov Decision Process (MDP)Hybrid Automatic Repeat reQuest (HARQ)latency optimizationreliability in 5Gproject