The coexistence of Enhanced Mobile Broad-band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services is a common scenario in 5G. While eMBB strives for high data rates using slots as transmission time intervals (TTIs), URLLC emphasizes reliability and low latency using mini-slots. Puncturing scheme is introduced by 3GPP, which means puncturing a set of Resource Blocks (RBs) from ongoing eMBB transmissions and reallocating them for URLLC traffic. In this paper, a DRL-based dynamic resource slicing scheme for eMBB and URLLC traffic considering puncturing is proposed, where the data rate, Quality of Service (QoS) satisfaction and rate stability for eMBB users are simultaneously optimized on mini-slot-level timescale, by employing an improved Deep Q-learning (DQN) algorithm. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms while ensuring the latency and reliability requirements of URLLC and successfully protects eMBB users under adverse conditions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning Based Dynamic Resource Slicing for eMBB and URLLC Traffic Considering Puncturing


    Contributors:
    Wenqi, Zhang (author) / Zhiwen, Pan (author) / Nan, Liu (author) / Xiaohu, You (author)


    Publication date :

    2024-06-24


    Size :

    1751921 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Reinforcement Learning-Based Resource Management for 5G Networks: Optimizing eMBB Throughput and URLLC Latency

    Pandey, Chandrasen / Tiwari, Vaibhav / Imoize, Agbotiname Lucky et al. | IEEE | 2023


    Non-Orthogonal Multiple Access and Network Slicing: Scalable Coexistence of eMBB and URLLC

    Tominaga, Eduardo Noboro / Alves, Hirley / Souza, Richard Demo et al. | IEEE | 2021


    Joint Resource Allocation for Multiplexing eMBB, URLLC and mMTC Traffics Based on DRL

    Ren, Rong / Wang, Jie / Yu, Jingming et al. | IEEE | 2024


    Efficient Low Complexity Packet Scheduling Algorithm for Mixed URLLC and eMBB Traffic in 5G

    Karimi, Ali / Pedersen, Klaus I. / Mahmood, Nurul Huda et al. | IEEE | 2019


    On-Demand Multiplexing of eMBB/URLLC Traffic in a Multi-UAV Relay Network

    Tian, Mengqiu / Li, Changle / Hui, Yilong et al. | IEEE | 2024