Communication System with Walsh Transform-Based End-to-End Autoencoder

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Detalles Bibliográficos
Publicado en:	Electronics vol. 14, no. 23 (2025), p. 4738-4759
Autor principal:	Knyva Mindaugas
Otros Autores:	Ruseckas Julius, Alfonsas, Juršėnas
Publicado:	MDPI AG
Materias:	Wireless communications Receivers & amplifiers Machine learning Simulation Deep learning Signal processing Neural networks Fourier transforms Walsh transforms Fast Fourier transformations Systems design Adaptation Basis functions Design Communications systems Orthogonal Frequency Division Multiplexing Software radio Signal to noise ratio
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	This paper investigates the design of end-to-end (E2E) autoencoders within AI-enhanced communication systems. It emphasizes the advantages of transitioning from Fast Fourier Transform (FFT)-based Orthogonal Frequency Division Multiplexing (OFDM) to a modulation technique based on the Walsh–Hadamard transform (WHT). This study underscores the WHT’s use of aperiodic basis functions, in contrast with the periodic bases of Fourier transforms. The proposed E2E autoencoder model integrates neural networks in both the transmitter and receiver for signal processing. The model is trained to adapt the bit rate according to the measured channel signal-to-noise ratio (SNR) using the same neural network, enabling operation at low SNR levels (down to −10 dB). Additionally, the model was experimentally validated in a laboratory setting using a software-defined radio (SDR)-based system setup.
ISSN:	2079-9292
DOI:	10.3390/electronics14234738
Fuente:	Advanced Technologies & Aerospace Database