Abstract
In this paper, the problem of energy-efficient unmanned aerial vehicle (UAV)-assisted computation offloading over the Terahertz (THz) spectrum is investigated. In the studied system, several UAVs are deployed as edge servers to aid task executions for multiple energy-limited computation-scarce terrestrial user equipments (UEs). Then, an expected energy efficiency maximization problem is formulated, aiming to jointly optimize UAVs’ trajectories, UEs’ local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UEs’ offloading powers. To tackle the considered multi-dimensional optimization problem, the duo-staggered perturbed actor-critic with modular networks (DSPAC-MN) solution in a multi-agent deep reinforcement learning (MADRL) setup, is proposed and tailored, after mapping the original problem into a stochastic (Markov) game. Compared to representative benchmarks in simulations, e.g., multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed DDPG (MATD3), the proposed DSPAC-MN can achieve the optimal performance of average energy efficiency, while ensuring 100% safe flights.
| Original language | English |
|---|---|
| Title of host publication | IEEE Conference on Global Communications (GLOBECOM) |
| Publisher | IEEE |
| Publication status | Published - Dec 2024 |
| Externally published | Yes |
Keywords
- Terahertz communications
- multi-access edge computing (MEC)
- unmanned aerial vehicle (UAV)
- deep reinforcement learning (DRL)
- energy-efficiciency
- trajectory optimization
- multi-agent deep reinforcement learning (MADRL)
- communication offloading
