Novel Online-Offline MA2C-DDPG for Efficient Spectrum Allocation and Trajectory Optimization in Dynamic Spectrum Sharing UAV Networks

TL;DR

This paper introduces a novel online-offline multi-agent actor-critic and deep deterministic policy-gradient framework for efficient spectrum sharing and trajectory optimization in UAV networks, addressing spectrum scarcity and interference issues.

Contribution

It proposes a new adaptive scheme combining online-offline learning for spectrum allocation and trajectory planning in UAV networks under jamming conditions.

Findings

Achieves the highest transmission rate among benchmarks.

Demonstrates high efficiency of the proposed framework.

Effectively adapts to various jamming models.

Abstract

Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications. However, it is susceptible to the severe spectrum scarcity problem and interference since it operates in the unlicensed spectrum band. In order to tackle those issues, a dynamic spectrum sharing network is considered with the anti-jamming technique. Moreover, an intelligent spectrum allocation and trajectory optimization scheme is proposed to adapt to diverse jamming models by exploiting our designed novel online-offline multi-agent actor-critic and deep deterministic policy-gradient framework. Simulation results demonstrate the high efficiency of our proposed framework. It is also shown that our proposed scheme achieves the largest transmission rate among all benchmark schemes.