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Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC

Authors:
Hasan Basri Celebi
Mikael Skoglund

Keywords: URLLC, reinforcement learning, quantized feedback, Rician-K estimation, goodput maximization.

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.

Pages: 4 to 9

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2308-4219

ISBN: 978-1-68558-232-6

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025