Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

Yan Liu,Maged Elkashlan,George K Karagiannidis,Yansha Deng,Arumugam Nallanathan

doi:10.1109/jsac.2022.3143264

Abstract

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

Highlights

In the standardization of the Fifth Generation (5G) New Radio (NR), three communication service categories were defined to address the requirements of novel Internet of Things (IoT) use cases [1]
We propose a Cooperative Multi-Agent learning technique based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of resources among multiple CGs (MCG) so as to maximize the number of successful transmissions under the latency constraint, which breaks down the selection of high-dimensional parameters into multiple parallel sub-tasks with a number of DDQN agents cooperatively being trained to produce each parameter
We proposed a novel MCG-GF-non-orthogonal multiple access (NOMA) learning framework for attaining the long-term successfully served user equipments (UEs) under the latency constraint in massive Ultra-Reliable and LowLatency Communications (URLLC) (mURLLC) service, where

Summary

Introduction

In the standardization of the Fifth Generation (5G) New Radio (NR), three communication service categories were defined to address the requirements of novel Internet of Things (IoT) use cases [1]. Considering the explosive increase in the number of IoT devices, it is essential to improve the access performance in networks for accommodating massive access with various requirements. Integrating URLLC with massive access, massive URLLC (mURLLC) wireless networks are able to realize efficient, delay-bounded, and reliable communications for a massive number of IoT devices [3]. Addressing the need in mURLLC is fundamentally challenging as it needs to simultaneously guarantee the latency, reliability, and massive access requirements

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Conclusion