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<p>A communication network can natively provide artificial intelligence (AI) training services for resource-limited network entities to quickly build accurate digital twins and achieve high-level network autonomy. Considering that network entities that require digital twins and those that provide AI services may belong to different operators, incentive mechanisms are needed to maximize the utility of both. In this paper, we establish a Stackelberg game to model AI training task offloading for digital twins in native AI networks with the operator with base stations as the leader and resource-limited network entities as the followers. We analyze the Stackelberg equilibrium to obtain equilibrium solutions. Considering the time-varying wireless network environment, we further design a deep reinforcement learning algorithm to achieve dynamic pricing and task offloading. Finally, extensive simulations are conducted to verify the effectiveness of our proposal.</p>

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Incentive-based task offloading for digital twins in 6G native artificial intelligence networks: a learning approach

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Tianjiao Chen,
  • Xiaoyun Wang,
  • Meihui Hua,
  • Qinqin Tang

摘要

A communication network can natively provide artificial intelligence (AI) training services for resource-limited network entities to quickly build accurate digital twins and achieve high-level network autonomy. Considering that network entities that require digital twins and those that provide AI services may belong to different operators, incentive mechanisms are needed to maximize the utility of both. In this paper, we establish a Stackelberg game to model AI training task offloading for digital twins in native AI networks with the operator with base stations as the leader and resource-limited network entities as the followers. We analyze the Stackelberg equilibrium to obtain equilibrium solutions. Considering the time-varying wireless network environment, we further design a deep reinforcement learning algorithm to achieve dynamic pricing and task offloading. Finally, extensive simulations are conducted to verify the effectiveness of our proposal.