UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning

TL;DR

This paper proposes a hybrid action reinforcement learning framework to optimize UAV-assisted semantic communication, enhancing data collection efficiency for remote metaverse users by balancing quality and energy costs.

Contribution

It introduces a novel hybrid RL approach that jointly optimizes semantic model scale, channel allocation, transmission power, and UAV trajectory for semantic communication.

Findings

Improves uplink data collection efficiency in simulations.

Outperforms benchmark methods in various scenarios.

Balances reconstruction quality and energy consumption effectively.

Abstract

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.