Securing radio resources allocation with deep reinforcement learning for IoE services in next-generation wireless networks

Peng, Yuhuai, Xue, Xiaojing, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0001-7595-2522, Zhu, Xiaogang, Al-Otaibi, Yasser D, Tariq, Usman and Yu, Keping (2022) Securing radio resources allocation with deep reinforcement learning for IoE services in next-generation wireless networks. IEEE Transactions on Network Science and Engineering, 9 (5). pp. 2991-3003. ISSN 2327-4697

Preview

Accepted Version Download (8MB) | Preview

Abstract

The next generation wireless network (NGWN) is undergoing an unprecedented revolution, in which trillions of machines, people, and objects are interconnected to realize the Internet of Everything (IoE). with the emergence of IoE services such as virtual reality, augmented reality, and industrial 5G, the scarcity of radio resources becomes more serious. Moreover, there are hidden dangers of untrusted terminals accessing the system and illegally manipulating interconnected devices. To tackle these challenges, this paper proposes a securing radio resources allocation scheme with Deep Reinforcement Learning for IoE services in NGWN. First, the solution uses a BP neural network based on multi-feature optimized Firefly Algorithm (FA) for spectrum prediction, thereby improving the prediction accuracy and avoiding interference between unauthorized and authorized users with efficient radio utilization. Then, a spectrum sensing method based on deep reinforcement learning is proposed to identify the untrusted users in system while fusing the sensing results, to enhance the security of the cooperative process and the detection accuracy of spectrum holes. Extensive simulation results show that the proposal is superior to the traditional solutions in terms of prediction accuracy, spectrum utilization and energy consumption, and is suitable for deployment in future wireless systems.