01612nas a2200145   4500008004100000245007400041210006900115520103500184100002201219700002101241700002401262700002201286700002601308856013201334       2023                        eng d00aReinforcement Learning Approach for Resource Allocation in 5G HetNets0 aReinforcement Learning Approach for Resource Allocation in 5G He3 aHeterogeneous Networks (HetNets) have been hailed as a critical technology for 5G communications, allowing for the rapid expansion of mobile traffic. HetNets can increase network capacity and serve additional users by installing small cells inside macrocells. However, resource allocation for such networks becomes more challenging than for conventional cellular networks due to interference between small-cells and macrocells, making it more difficult to provide quality of service. Deep Reinforcement Learning (DRL) has opened the door for applications in resource allocation for 5G HetNets, because of recent breakthroughs in the field. We present a unique resource allocation technique based on DRL that may be used to both small and macro cells. According to the resource allocation process, an autonomous “agent”, in our case a cell, makes judgments to determine the appropriate BS to assign to a user, and the optimal number of Resource Blocks that should be allocated, while not needing or waiting for any information.1 aAllagiotis, Fivos1 aBouras, Christos1 aKokkinos, Vasileios1 aGkamas, Apostolos1 aPouyioutas, Philippos  uhttp://telematics.upatras.gr/telematics/publications/reinforcement-learning-approach-resource-allocation-5g-hetnets?language=el