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With the growing number of mobile devices (MDs), users can achieve higher quality of service (QoS) by offloading latency-sensitive tasks to edge access points (E-APs). However, the number of MDs that E-APs can serve is limited. Consequently, the existing network cannot fully meet users’ real-time demands. In recent years, advancements in terahertz (THz) band technology, with its ultra-high data transmission rate and bandwidth, have opened new possibilities for addressing this issue. This paper explores a unmanned aerial vehicle (UAV) assisted computation offloading network utilizing THz technology. The study examines computation offloading and resource allocation under constraints, including energy consumption and resource availability. A Deep Deterministic Policy Gradient (DDPG) based resource allocation and offloading and UAV Placement Algorithm (DRAOP) is proposed to find the optimal solution in a stochastic dynamic environment. Simulation results demonstrate the effectiveness of the proposed scheme.

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DRL-Based Computation Offloading and Resource Allocation in THz Band

  • 会议论文

  • 发表日期：

    2025年

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  • Jiyuan Wei,
  • Xin Chen,
  • Libo Jiao,
  • Long Zhao

摘要

With the growing number of mobile devices (MDs), users can achieve higher quality of service (QoS) by offloading latency-sensitive tasks to edge access points (E-APs). However, the number of MDs that E-APs can serve is limited. Consequently, the existing network cannot fully meet users’ real-time demands. In recent years, advancements in terahertz (THz) band technology, with its ultra-high data transmission rate and bandwidth, have opened new possibilities for addressing this issue. This paper explores a unmanned aerial vehicle (UAV) assisted computation offloading network utilizing THz technology. The study examines computation offloading and resource allocation under constraints, including energy consumption and resource availability. A Deep Deterministic Policy Gradient (DDPG) based resource allocation and offloading and UAV Placement Algorithm (DRAOP) is proposed to find the optimal solution in a stochastic dynamic environment. Simulation results demonstrate the effectiveness of the proposed scheme.