Data Service Maximization in Space-Air-Ground Integrated 6G Networks

TL;DR

This work tackles data service maximization in space-air-ground integrated networks (SAGIN) by jointly optimizing user association, aerial-user trajectories, and base-station power under spectrum sharing and QoS constraints. It solves a challenging mixed-integer non-convex problem through a two-subproblem decomposition: (i) binary association via a Gurobi-based binary integer program and (ii) trajectory and power optimization via a deep deterministic policy gradient (DDPG) approach, with an alternating block coordinate descent to reach convergence. The framework demonstrates substantial gains over baselines, achieving up to 51.6% improvement over random association and notable improvements over learning-based trajectory methods and fixed-power schemes. The results underscore SAGIN’s potential to deliver high data service and robust QoS in 6G networks through intelligent interference management and joint optimization.

Abstract

Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of the reliability of terrestrial networks, broad coverage and service continuity of non-terrestrial networks like low earth orbit satellites (LEOSats), etc. In this work, we study a data service maximization problem in space-air-ground integrated network (SAGIN) where the ground base stations (GBSs) and LEOSats cooperatively serve the coexisting aerial users (AUs) and ground users (GUs). Then, by considering the spectrum scarcity, interference, and QoS requirements of the users, we jointly optimize the user association, AU's trajectory, and power allocation. To tackle the formulated mixed-integer non-convex problem, we disintegrate it into two subproblems: 1) user association problem and 2) trajectory and power allocation problem. We formulate the user association problem as a binary integer programming problem and solve it by using the Gurobi optimizer. Meanwhile, the trajectory and power allocation problem is solved by the deep deterministic policy gradient (DDPG) method to cope with the problem's non-convexity and dynamic network environments. Then, the two subproblems are alternately solved by the proposed block coordinate descent algorithm. By comparing with the baselines in the existing literature, extensive simulations are conducted to evaluate the performance of the proposed framework.

Figures (4)

• Figure 1: Space-air-ground integrated 6G network.
• Figure 2: Optimal trajectories of AUs under different algorithms.
• Figure 3: Total downloaded data with different number of users.
• Figure 4: Cumulative reward for different power values.