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

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

Distributed Learning-Based Multi-Band Multi-User Cooperative Sensing in Cognitive Radio Networks

Multi-band cooperative spectrum sensing can provide access to a wide range of spectrum in cognitive radio networks (CRNs). The design of multi-band spectrum sensing is very challenging mainly due to scheduling of secondary users (SUs) to sense a subset of channels. In this paper, we propose a distributed learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme to select most appropriate SUs to sense channels. The proposed scheme allows SUs to sense multiple channels, and consists of two stages: 1) leader selection for each channel, and 2) selection of corresponding cooperative SUs to sense these channels. We formulate an optimization problem to select leaders that can effectively communicate with other SUs subject to the constraint that each SU can act as a leader for only one channel, and there will be only one leader for each channel. We then formulate another optimization problem to select corresponding cooperative SUs for each channel. After this stage, selected cooperative SUs sense channels, and use consensus learning to determine the availability of channels in a distributed manner. Simulation results show that the proposed M2CSS scheme can enhance detection performance, avoid the choice of redundant cooperative SUs, owning similar sensed information, and provide fair energy consumption for all channels compared to the existing schemes