Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC

2025/01/19 by Hasan Basri Çelebi, Celebi, Hasan Basri, Mikael Skoglund +1
Chemistry · Engineering · Environmental Science · #Electrochemical Analysis and Applications #Electrochemical sensors and biosensors #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Water Quality Monitoring and Analysis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2501.11190

openalex publication_date 2025/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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