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Deep Learning Network for Multiuser Detection in Satellite Mobile Communication System.

Guan Qing Yang1,2, Wu Shuang1, He Ya-Ru1

  • 1College of Engineering, Xi'an International University, Xi'an 710077, China.

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A deep learning network improves satellite mobile communication by enhancing multiuser detection (MUD). This novel algorithm reduces interference and boosts signal accuracy, outperforming traditional methods for clearer satellite links.

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Area of Science:

  • Satellite communication systems
  • Deep learning applications
  • Signal processing

Background:

  • Satellite mobile communication systems face performance degradation due to multiple access interference (MUI) caused by multipath fading and relative motion.
  • Traditional multiuser detection (MUD) algorithms like serial and parallel interference cancellation struggle to effectively mitigate MUI in dynamic satellite environments.

Purpose of the Study:

  • To propose a novel deep learning network-based multiuser detection (MUD) algorithm for satellite mobile communication systems.
  • To enhance system performance by effectively mitigating multiple access interference (MUI) and improving signal-to-noise ratio (SNR).

Main Methods:

  • Developed a deep learning network to establish an optimal Carrier-to-Interference-plus-Noise Ratio (CINR) loss function tailored for multiuser access modes.
  • Employed steepest gradient iteration for optimizing multiuser detection weights and multilayer nonlinear learning for interference cancellation sharing weights.
  • Utilized multilayer network forward learning iteration to obtain multiuser detection weights based on traditional quality characteristics.

Main Results:

  • The proposed deep learning MUD algorithm demonstrated superior performance compared to traditional serial and parallel interference cancellation algorithms.
  • Achieved maximum signal-to-noise ratio through iterative gradient optimization and interference cancellation sharing weights.
  • The algorithm significantly improved MUD accuracy and reduced the complexity associated with traditional multiuser detection methods.

Conclusions:

  • The proposed deep learning network-based MUD algorithm offers a highly precise and efficient solution for satellite mobile communication systems.
  • The method provides better iteration times and enhanced performance in satellite multifading uplink scenarios.
  • This deep learning approach effectively addresses the challenges of MUI in satellite communications, paving the way for improved system reliability.