HIGHLIGHTS
SUMMARY
Better spectrum sensing approaches are made possible via the use of deep learning models such as convolutional neural_networks (CNN) and long shortterm memory (LSTM). To overcome this constraint, the authors suggest a unique hybrid deep learning model, which combines the best features of both LSTM and extreme_learning_machines (ELM), for improving the spectrum sensing approach. This paper proposes "DLSenseNet," a DL-based model for spectrum sensing in which the structure information of received modulated signals is used for the purpose of spectrum sensing. The performance of the convolutional neural_network (CNN) has been enhanced to recognize . . .
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