MADDPG-M&L: UAV-Assisted Joint User Association and Slicing Resource Allocation in HetNets

Abstract

With the increasing diversity of use cases and service requirements in heterogeneous networks, the concept of network slicing has emerged. However, user association, distributed resource allocation, and the high-speed data rate demands of different users still face numerous challenges. To address these issues, we propose a UAV-assisted RAN resource slicing framework in heterogeneous networks. Firstly, we employ a stable matching game algorithm to solve the access problem between UAVs (unmanned aerial vehicles) and TBSs (terrestrial base stations). Secondly, we formulate a joint user association and slicing resource allocation problem. However, the optimization problem is non-convex, and the problem is decoupled into two sub-problems: user association and slicing resource allocation. Moreover, a Lagrangian dual algorithm is employed to solve the user association problem, while Multi-Agent Deep Deterministic Policy Gradient based on Matching Game and Lagrangian Dual (MADDPG-M&L) slicing resource allocation algorithm is proposed to determine the allocation ratio of resources for each slice. Simulation results show that the Lagrangian dual-based user association algorithm improves the system performance by 12.8%, 36.2% and 61.9% respectively compared to the other three user association methods. Furthermore, compared to MATD3-M&L, MASAC-M&L, and Hard-slicing, the proposed MADDPG-M&L algorithm improves the throughput by 36.3%, 105%, and 177%, respectively. In terms of latency, the improvements are 46%, 68%, and 86.7%, respectively. For SINR, the increases are 5.2%, 2.9%, and 6.4%, respectively. The objective function improves by 54.7%, 218%, and 336%, respectively, with the data transmission rate showing the most significant improvement.