is paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users' requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. en, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell-Hestenes-Rockafellar method and the branch and bound method, obtaining the optimal solution. e simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users' requirements change of two slices and get better spectral efficiency performance. methods to meet the different requirements of services with multiple logical networks. It has been a key enabler for 5G to accommodate a variety of services in a flexible manner [3]. erefore, it is critical to study multiservice problems in the virtual radio access network (RAN) by network slicing, especially different service requirements in various scenarios, such as enhanced mobile broadband (eMBB), ultrareliable low latency communication (URLLC), and massive machine-type communication (mMTC) [4,5]. e network slicing architecture consists of a core network (CN) and RAN slicing. Since the research of network slicing in the CN has been relatively mature and our research focuses on RAN slicing, the work of CN slicing is briefly introduced. For instance, in [6], the authors investigated how to combine fog node and network slicing of CN to safely access remote service data while ensuring low

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With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on under1 lay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

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Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay-underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.