NeuroDetect: Deep Learning-Based Signal Detection in Phase-Modulated Systems with Low-Resolution Quantization.

Journal: Sensors (Basel, Switzerland)
Published Date: May 19, 2025

Abstract

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case 12% margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems.

Authors

  • Chanula Luckshan
    Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.
  • Samiru Gayan
    Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.
  • Hazer Inaltekin
    School of Engineering, Macquarie University, North Ryde, NSW 2109, Australia.
  • Ruhui Zhang
    Institute of Advanced Study in Mathematics, Harbin Institute of Technology, Harbin 150001, China.
  • David Akman
    Lifelong Learning, University of New South Wales, Kensington, NSW 2052, Australia.

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