Intelligent Resource Allocation in Wireless Communications Systems The emergence of DL represents a potential paradigm shift in the design of WCS, from conventional handcrafted schemes based on mathematical models with assumptions, to autonomous schemes based on DL using large sets of data. In this article, we discuss the essential elements of DL and investigate an intelligent RA scheme based on a DNN, in which multiple goals with various constraints can be satisfied through DL. Having confirmed the optimality and feasibility of DNNbased RA through simulation, we discuss some of the key technical challenges that remain problematic for the application of DL in the practical application of WCS.

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.