Analyzing Grant-Free Access for URLLC Service
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Abstract (arXiv)
5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grantbased (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper. Specifically, we present a spatiotemporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2020.
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Citing papers per year: 2022: 2; 2023: 2.
4 papers.
- 2022Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approachcited by 3
- 2022Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?cited by 2
- 2023Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networkscited by 0
- 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoTcited by 0
- 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT21 Nov 2023
- 2023Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks28 Aug 2023
- 2022Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach22 Nov 2022
- 2022Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?11 Jul 2022