Energy Efficient UAV-Enabled Multicast Systems: Joint Grouping and Trajectory Optimization

Abstract

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a file of common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning ((CML)-M-2) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one-dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the (CML)-M-2-enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA-TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline (CML)-M-2-enabled multicast grouping and the online CA-TSP inspired UAV trajectory optimization, is shown to achieve excellent energy-saving performance.