Adaptive Finite Blocklength for Low Access Delay in 6G Wireless Networks
<|reference_start|>Adaptive Finite Blocklength for Low Access Delay in 6G Wireless Networks: As the number of real-time applications with ultra-low delay requirements quickly grows, massive ultra-reliable and low-latency communication (mURLLC) has been proposed to provide a wide range of delay-sensitive services for the sixth generation (6G) wireless networks. However, it is difficult to meet the stringent delay demand of massive connectivity with existing grant-based (GB) random access and fixed frame structure in long-term evolution (LTE) and the fifth generation (5G) new radio (NR) systems. To solve this problem, in this paper we propose the new grant-free (GF) based adaptive blocklength scheme for short packet transmission to reduce the access delay. We develop the adaptive blocklength framework where the blocklength can be adaptively changed according to the real-time load, to revise the traditional non-flexible frame structure which impacts the delay performance. Taking the features of mURLLC into consideration, we analyze the GF random access procedure, packet arrival behavior, packet collision, and packet transmission error in the finite blocklength (FB) regime. On this basis, we derive the closed-form expression of successful access and transmission probability and give the GF-based status update model. Then, we propose the access delay minimization problem that jointly considers queuing delay and transmission delay to reduce the overall access delay. With the alternating optimization algorithm, we obtain the optimal blocklength of each packet, thus forming the corresponding adaptive blocklength scheme for mURLLC. Simulation results verify the correctness of theoretical results and show that our proposed adaptive blocklength scheme can significantly reduce the access delay compared with that of LTE and 5G NR systems.<|reference_end|>
@article{zhang2024adaptive,
    title={Adaptive Finite Blocklength for Low Access Delay in 6G Wireless Networks},
    author={Yixin Zhang, Wenchi Cheng, and Wei Zhang},
    journal={GLOBECOM 2022 - 2022 IEEE Global Communications Conference},
    year={2024},
    doi={10.1109/GLOBECOM48099.2022.10001372},
    archivePrefix={arXiv},
    eprint={2407.12653},
    primaryClass={cs.IT math.IT}
}