Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC
This addresses the challenge of reliable and low-latency communication in URLLC for wireless networks, but it is incremental as it builds on existing feedback and RL methods.
The paper tackles the problem of maximizing goodput in URLLC systems with quantized feedback under dynamic channel conditions by using reinforcement learning to adapt the feedback scheme, resulting in increased overall performance suitable for practical applications.
This paper presents a comprehensive system model for goodput maximization with quantized feedback in Ultra-Reliable Low-Latency Communication (URLLC), focusing on dynamic channel conditions and feedback schemes. The study investigates a communication system, where the receiver provides quantized channel state information to the transmitter. The system adapts its feedback scheme based on reinforcement learning, aiming to maximize goodput while accommodating varying channel statistics. We introduce a novel Rician-$K$ factor estimation technique to enable the communication system to optimize the feedback scheme. This dynamic approach increases the overall performance, making it well-suited for practical URLLC applications where channel statistics vary over time.