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Intelligent Deep Learning Based Speech Receivers

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This paper discusses the design, training, and comparison of various neural networks for creating a Deep Learning (DL)-based alternative to the conventional digital communication speech receiver for baseband demodulation. The models under consideration include Long Short-Term Memory (LSTM), 1-dimensional convolutional neural networks (Conv1D), and 2-dimensional convolutional neural networks (Conv2D). These models are robust enough for the reliable recovery of a practical A-law algorithm-based PCM-encoded speech signal done using a Spartan 6 mini Field Programmable Gate Array (FPGA) board, which is then bipolarly signaled and sent over an Additive White Gaussian Noise (AWGN) communication channel. Various time domain statistics of the speech signal, like Short-Term Energy (STE), Zero Crossing Rate (ZCR), autocorrelation, and Bit Error Rate (BER), are measured and compared with those of a conventional receiver subjected to noise with a Signal-to-Noise Ratio (SNR) in the range of -50 dB to 20 dB. The simulation results show better performance of the DL model, which promises reduced computational and hardware complexities in the future of signal detection in communication systems.
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Deep Learning (DL); Long Short-Term Memory (LSTM); 1-Dimensional Convolutional Neural Network (Conv1D) and 2-Dimensional Convolutional Neural Network (Conv2D); PCM; Short-Term Energy (STE); Zero Crossing Rate (ZCR); AutoCorrelation; Bit Error Rate (BER)

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