Multiple Access Integrated Adaptive Finite Blocklength for Ultra-Low Delay in 6G Wireless Networks
Yixin Zhang, Wenchi Cheng, and Wei Zhang

TL;DR
This paper proposes an adaptive finite blocklength framework with a multi-agent deep Q-network to minimize over-the-air delay in 6G wireless networks, supporting ultra-low latency for real-time applications.
Contribution
It introduces a novel adaptive blocklength scheme combined with a deep reinforcement learning approach for delay optimization in 6G mURLLC scenarios.
Findings
Outperforms LTE and 5G NR delay schemes
Effectively balances delay components through adaptive blocklength
Demonstrates significant delay reduction in simulations
Abstract
Facing the dramatic increase of real-time applications and time-sensitive services, large-scale ultra-low delay requirements are put forward for the sixth generation (6G) wireless networks. To support massive ultra-reliable and low-latency communications (mURLLC), in this paper we propose an adaptive finite blocklength framework to reduce the over-the-air delay for short packet transmissions with multiple-access and delay-bounded demands. In particular, we first give the specified over-the-air delay model. Then, we reveal the tradeoff relationship among queuing delay, transmission delay, and the number of retransmissions along with the change of finite blocklength, as well as formulate the adaptive blocklength framework. Based on the adaptive blocklength framework and associated with grant-free (GF) access protocol, we formulate the average over-the-air delay minimization problem, where…
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