An Actor-Critic Deep Reinforcement Learning Model with Energy-Awareness and Latency Minimization for Dynamic Spectrum Allocation in 6G-Enabled Aerial Mobile Wireless Networks

Abstract

Aerial Mobile Wireless Networks (AMWN) play an important role in next-generation communication networks, especially in military operations, disaster recovery, and real-time surveillance. However, the highly dynamic nature of AMWN creates significant challenges for dynamic spectrum allocation(DSA), latency control, energy conservation, and spectrum optimization. These challenges become more critical in 6G-enabled environments that require ultra-low latency, high energy efficiency, and large-scale device connectivity. Deep Reinforcement Learning (DRL) offers a powerful approach by enabling real-time, data-driven decision-making in complex environments. This paper presents an Actor-Critic Deep Reinforcement Learning (AC-DRL) model adapted to a swarm-based behavior model for dynamic spectrum allocationwith energy awareness and latency minimization in AMWN. The AC-DRL model is evaluated against a standard Q-learning approach using a custom dataset. The results show improved latency reduction, spectral efficiency, and energy consumption. Simulation results demonstrate up to 27% latency reduction and 22% improvement in energy efficiency compared with traditional models.