UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning
This work addresses data collection challenges for metaverse users in remote areas, but it appears incremental as it builds on existing UAV-assisted and semantic communication methods with a hybrid RL approach.
The paper tackled the problem of inefficient uplink data collection for metaverse users in remote areas by proposing a hybrid action reinforcement learning framework to optimize semantic model scale, channel allocation, transmission power, and UAV trajectory, resulting in improved efficiency under various parameter settings compared to benchmarks.
In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas. To reduce the time for uplink data collection while balancing the trade-off between reconstruction quality and computational energy cost, we propose a hybrid action reinforcement learning (RL) framework to make decisions on semantic model scale, channel allocation, transmission power, and UAV trajectory. The variables are classified into discrete type and continuous type, which are optimized by two different RL agents to generate the combined action. Simulation results indicate that the proposed hybrid action reinforcement learning framework can effectively improve the efficiency of uplink semantic data collection under different parameter settings and outperforms the benchmark scenarios.