Improved Ant Colony Optimization routing algorithm for UAV ad-hoc Network based on Link Quality Prediction

Abstract:

Unmanned Aerial vehicle ad-hoc Network(UANET) can increase the communication range by multi-hop forwarding, in which the routing algorithm undertakes the task of packet transmission path planning. To address the gain attenuation problem caused by inaccurate directional antenna beam pairing due to UAV positioning deviation in highly dynamic networks, an Ant Colony Optimization routing algorithm based on Link Quality Prediction(LQP-ACO) is proposed. The algorithm first predicts the link quality between UAV nodes using Bidirectional Gated Recurrent Unit-Fully Connected Neural Network(BiGRU-FCNN). Then, based on the predicted link quality, ant colony optimization algorithm is employed to find the two optimal paths for business data transmission. Simulation results show that the routing algorithm proposed in this paper reduces the packet loss rate by 2.75% and 4.5% respectively compared to the traditional Dijkstra's algorithm under Random Way Point(RWP) as well as Random Walk(RW) mobile models.