Quantum-Based Deep Q-Network Bandwidth Resource Allocation Algorithm for UASN

Resource allocation faces significant challenges due to the complexity of the underwater environment. To address the problem of bandwidth assignment and improve the underwater resource utilization efficiency, this article proposes a quantum-based deep Q-network resource allocation algorithm. First, this algorithm combines factors, such as signal-to-noise ratio and data amount to construct the state space, which can better disclose the interaction between learning and environment. It also designs a unique reward function, which can guide nodes to select appropriate bandwidth, thus improving the learning capability of the deep reinforcement learning model. Furthermore, this article constructs a hybrid network model based on trainable quantum circuits, which fully utilizes various quantum gate operations to process and analyze data, predict the corresponding Q-values for actions. Simulation results show that the algorithm can reduce packet loss ratio and blocking probability while improving network bandwidth utilization.