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Title |
Intelligent Receiver for Frequency Hopping Signals Using Deep Learning |
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Author |
Mahmoud M. Qasaymeh, Ali A. Alqatawneh, and Ahmad F. Aljaafreh |
| Citation |
Vol. 25 No. 2 pp. 188-196 |
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Abstract |
This paper presents a promising Deep-Learning (DL) based approach for accurate symbol detection in a Slow Frequency Hopping (SFH) wireless communication system under a Narrow Band (NB) multipath channel fading. A feedforward neural network with three layers of input, hidden, and output was employed for deep learning. The neural network is designed to take 80 features as input, representing the received signal samples at the receiver. The neural network is trained to anticipate the transmitted symbol based on the provided training dataset, employing the Adaptive Moment Estimation (Adam) optimizer alongside categorical cross entropy as the loss function. Additionally, computer simulations are conducted to verify the effectiveness of the proposed method across various modulation schemes. |
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Keywords |
Frequency Hopping, Time Delay Estimation, Channel gains, a narrow band (NB) multipath channel, Confusion Matrix Machine Learning, Categorical Cross-Entropy Loss, Neural Network, Loss function. |
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