A DRLbased resource allocation framework for multimedia multicast in 5G cellular networks Cloud Radio Access Network (CRAN) is a key enabling network architecture for the next generation (5G) wireless communications and has advantages of providing multimedia service on the basis of properly resource allocation. However, the resource allocation strategy still needs to be further improved in order to allocate resource where unicast or multicast multimedia services are requested simultaneously. Aiming at minimizing power consumption and meeting quality of service (QoS) requirement of users, this paper proposes a deep reinforcement learning based unicastmulticast resource allocation framework (DRLCRAF) for it with high energy efficiency. Specifically, we define the state space, action space and reward function for the DRL agent, and formally formulate the resource allocation problem in a multimedia service scenario as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with three widelyused baselines via simulation. Simulation results show that the proposed method can not only meet the requirements of both unicast and multicast users but also effectively reduce 10% transmission energy consumption compared to full connectivity method.

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.