In this paper, we investigate timely and resource-efficient transmission for mixed traffic in industrial Internet-of-Things (IIoT) with short packet communication (SPC). Due to the limited resources in IIoT, fixed resource allocation for multiple users with different arrival rates leads to low resource utilization and high age of information (AoI). We formulate an optimization problem to minimize the required bandwidth for mixed traffic with timely constraints. To avoid resource waste and data collisions, we use traffic prediction to categorize users into high and low traffic states. For users in high traffic state, dedicated bandwidth is reserved for each user while a synchronous multi-channel slotted ALOHA access method is adopted for users in low traffic state. To meet the required timeliness requirements, the closed-form expressions of the peak AoI (PAoI) for users with SPC in different traffic states are derived. Then we explore an Traffic Classification-based Bandwidth Allocation (TCBA) algorithm to minimize the required bandwidth with the timely constraints of mixed traffic. Numerical results are provided to verify our analysis and demonstrate that the proposed TCBA algorithm outperforms the existing methods significantly in terms of bandwidth saving.


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    Titel :

    AoI-Aware Adaptive Access and Bandwidth Allocation in IIoT with Mixed Traffic and Finite Blocklength


    Beteiligte:
    Zhou, Zhekang (Autor:in) / Cao, Jie (Autor:in) / Zhu, Xu (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    405980 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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