Retransmission Aware Adaptive Modulation and Coding Toward Deterministic Delay Performance

Ultra-reliable low-latency communication (URLLC) is an indispensable element towards supporting various latency-sensitive and reliability-critical applications. To optimize the average delay while satisfying the deterministic delay constraint, initial transmission and retransmission should be handle...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 23; no. 11; pp. 17683 - 17697
Main Authors: Jin, Yuze, Wang, Wei, Wang, Yitu, Yin, Rui, Zheng, Ziwei, Zhang, Zhaoyang
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
adaptive modulation and coding
Bellman theory
Coding
Constraints
Couplings
data retransmission
Delay
Delays
deterministic delay constraint
Markov processes
Modulation
Optimization
Queuing theory
Signal to noise ratio
Throughput
Ultra reliable low latency communication
URLLC
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Ultra-reliable low-latency communication (URLLC) is an indispensable element towards supporting various latency-sensitive and reliability-critical applications. To optimize the average delay while satisfying the deterministic delay constraint, initial transmission and retransmission should be handled with different priorities due to the differentiated urgency, which creates complex interdependency and brings new technical challenges to delay-oriented optimization. In this paper, we propose a retransmission-aware adaptive modulation and coding (RAMC) scheme to improve the delay performance in URLLC scenarios. Specifically, we first establish a cascaded queue system, including an initial transmission queue and a retransmission queue. The deterministic delay constraint is satisfied through Lyapunov optimization, where we transform the Lyapunov drift-plus-penalty problem into an infinite horizon Markov decision process (MDP) by constructing a renewal system with sampling to overcome the challenge brought by queue coupling. Next, we propose the delay-optimal RAMC scheme by solving the associated Bellman equation by improved reinforcement learning, which is proved to be asymptotically optimal. Finally, the superiority of the proposed RAMC scheme is verified through simulations.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3456188