RealTime Resource Slicing for 5G RAN via Deep Reinforcement Learning With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important technology to support the multiusersu0027 needs for 5G mobile network. Network slicing allows network operators to provide services to different users, which can improve the rational utilization of network and hardware resources. In order to ensure the quality of service and build lowcost network infrastructure services, it is a challenging problem to find an appropriate resource allocation mechanism. In this paper, we discuss resource allocation in 5G radio access network (RAN). Considering the realtime resource request of the slice user, we propose a semiMarkov decision system model, which enables the virtual network provider to effectively satisfy the different user demands in real time. Then, we propose a resource slicing algorithm based on deep reinforcement learning (RSDRL), which aims to improve the longterm benefits of virtual network providers and the utilization of slicing resources. We evaluate the performance of the RSDRL through evaluations and comparisons. The results show that the proposed RSDRL algorithm can effectively improve the performance and achieve the longterm benefits quickly.

GANpowered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demandaware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial networkpowered deep distributional Q network (GANDDQN) to learn the actionvalue distribution driven by minimizing the discrepancy between the estimated actionvalue distribution and the target actionvalue distribution. We put forward a rewardclipping mechanism to stabilize GANDDQN training against the effects of widelyspanning utility values. Moreover, we further develop Dueling GANDDQN, which uses a specially designed dueling generator, to learn the actionvalue distribution by estimating the statevalue distribution and the action advantage function. Finally, we verify the performance of the proposed GANDDQN and Dueling GANDDQN algorithms through extensive simulations.