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

Ghaida  Muttashar Abdulsahib; Kholoud Alkayid Alkayid; Muhammad  Asshad; Ahmed A F Osman; Mohammed Awad  Mohammed Ataelfadiel; Ashraf Mahrous Nour Zaher

Authors

Ghaida Muttashar Abdulsahib College of Computer Engineering, University of Technology, Baghdad 10066, IRAQ
Kholoud Alkayid Department of Computer Science, Faculty of Information Technology, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, JORDAN
Muhammad Asshad Computer Networks, Faculty of Computer Studies, Arab Open University, OMAN
Ahmed A. F Osman Applied College, King Faisal University, P.O. Box 400, Al-Ahsa 31982, KINGDOM OF SAUDI ARABIA
Mohammed Awad Mohammed Ataelfadiel Applied College, King Faisal University, P.O. Box 400, Al-Ahsa 31982, KINGDOM OF SAUDI ARABIA
Ashraf Mahrous Nour Zaher College of Arts, King Faisal University,⁠ Al Ahsa 31982, Saudi Arabia

Keywords:

Aerial Mobile Wireless Network (AMWN), Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), Actor-Critic architecture, 6G Networks, Dynamic Spectrum Allocation, Latency Minimization, Energy Efficiency

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 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.