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Quantum-Based Deep Q-Network Bandwidth Resource Allocation Algorithm for UASN
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DOI：10.1109/JIOT.2024.3449045.png)
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摘要
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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.
Keyword:
Resource management
Bandwidth
Underwater acoustics
Internet of Things
Prediction algorithms
Channel allocation
Accuracy
Bandwidth resource allocation
deep Q-network (DQN)
hybrid quantum neural network (QNN)
underwater acoustic sensor networks (UASNs)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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