Event Details
5G NR PDSCH Adaptive Transmission Model for URLLC using DQN-Based Reinforcement Learning Solution
Presenter: Thet Naung San
Supervisor:
Date: Mon, October 14, 2024
Time: 08:30:00 - 00:00:00
Place: Zoom, link below.
ABSTRACT
Abstract: The increasing demand for low-latency, high-reliability communications in 5G New Radio (NR) is driving the need for optimized transmission technologies, especially for Ultra-Reliable Low-Latency Communication (URLLC) applications. This project introduces a Deep Q-Network (DQN) agent that intelligently adjusts the parameters of modulation and coding scheme (MCS) and numerology adaptively in real time to improve physical downlink shared channel (PDSCH) transmissions. The system simulates frequency-selective TDL-C fading channels with a Hybrid Automatic Repeat Request (HARQ) mechanism and optimizes based on packet size, signal-to-noise ratio (SNR), latency and number of HARQ retransmissions. The results show improved transmission efficiency and reliability, providing better performance for mission-critical 5G URLLC applications.
Thet Naung San is inviting you to a scheduled Zoom meeting.
Topic: ECE Graduate Seminar - Thet Naung San
Time: Oct 14, 2024 08:30 AM Pacific Time (US and Canada)
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Meeting ID: 842 5035 0747
Password: 112477
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