Adaptive Modulation and Coding for URLLC Retransmission

For ultra-reliable low-latency communication (URLLC), retransmission data should be scheduled with different priorities due to the urgency, which brings new challenges in delay-oriented optimization, especially with deterministic delay constraint. In this paper, we propose an adaptive modulation and...

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Bibliographic Details
Published in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1 - 6
Main Authors: Jin, Yuze, Wang, Wei, Zheng, Ziwei, Wang, Yitu, Zhang, Zhaoyang
Format: Conference Proceeding
Language:English
Published: IEEE 21.04.2024
Subjects:
Encoding
Markov decision processes
Modulation
Reinforcement learning
Simulation
Transforms
Ultra reliable low latency communication
ISSN:1558-2612
Online Access:Get full text
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Summary:For ultra-reliable low-latency communication (URLLC), retransmission data should be scheduled with different priorities due to the urgency, which brings new challenges in delay-oriented optimization, especially with deterministic delay constraint. In this paper, we propose an adaptive modulation and coding (AMC) scheme for both initial transmissions and retransmissions in separate data queues. Different from most of the existing works focusing on average delay, we jointly consider delay performance and deterministic delay requirement by combining the Markov decision process (MDP) with Lyapunov optimization technique. To overcome the coupling in the objective and the deterministic constraint, we transform this problem into an infinite horizon MDP by constructing a renewal system with sampling. Based on this, we propose a delay-optimal modulation and coding scheme (MCS) selection policy using reinforcement learning. Simulation results show that the proposed scheme achieves better delay performance than the conventional AMC schemes.
ISSN:1558-2612
DOI:10.1109/WCNC57260.2024.10571263