In this paper, we present an approach for resource scheduling in wireless networks based on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) Reinforcement Learning (RL) algorithm. More specifically, we propose a joint power and Scheduling Block (SB) allocation algorithm for networks with NS. The reinforcement learning algorithm applied to the resource allocation problem is formulated using state transitions regarding the system dynamics. We also present an algorithm, namely Network Slicing based on Reinforcement Learning (NSRL) that combines the proposed reinforcement learning based resource allocation with an approach based on reservation and sharing of resources among the slices where each RL agent acts in one slice. Simulations are carried out considering User Equipments (UEs) within a small cell coverage area - (Small Cells) with different Modulation and Coding Schemes (MCS) standardized by the 3rd Generation Partnership Project (3GPP) based on a simplified NS scenario with fifth generation wireless network (5G) characteristics. In the simulations, two slices are considered for the UEs: one considering Ultra-reliable and Low Latency Communications (URLLC) and other related to enhanced Mobile Broadband (eMBB) services. Simulation results show that the NSRL algorithm efficiently allocates power and SBs, outperforming other algorithms in the literature.

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms.