LAUSR

Heterogeneous cellular networks (HetNets) have been proven as a promising approach to deal with ever-growing data traffic. Supporting ultra-reliable and low-latency communication (URLLC) is also considered as a new feature of the upcoming wireless networks. Due to the overlapping structure and the mutual interference between cells in HetNets, existing resource allocation approaches cannot be directly applied for real-time applications, especially for URLLC services. As a novel unsupervised algorithm, Deep Q Network (DQN) has already been applied to many online complex optimization models successfully. However, it may perform badly for resource allocation optimization in HetNets, due to the tiny state change and the large-scale action space characteristics. In order to cope with them, we first propose an auto-encoder to disturb the similarity of adjacent states to enhance the features and then divide the whole decision process into two phases. DQN is applied to solve each phase, respectively, and we iterate the whole process to find the joint optimized solution. We implement our algorithm in 6 scenarios with different numbers of user equipment (UE), redundant links, and sub-carriers. Simulations results demonstrate that our algorithm has good convergence for the optimization objective. Moreover, by further optimizing the power allocation, a 1–2 nines of reliability improvement is obtained for bad conditions. Finally, the experiment result shows that our algorithm reaches the reliability of 8-nines in common scenarios. As an online method, the algorithm proposed in this paper takes only 0.32 s on average.