An Actor-Critic Deep Reinforcement Learning Model with Energy-Awareness and Latency Minimization for Dynamic Spectrum Allocation in 6G-Enabled Aerial Mobile Wireless Networks - Muttashar Abdulsahib, Ghaida; Alkayid, Kholoud Alkayid; Asshad, Muhammad; F Osman, Ahmed A; Mohammed Ataelfadiel, Mohammed Awad; Nour Zaher, Ashraf Mahrous (Journal of Soft Computing and Data Mining, 2026)
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 allocation with 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.