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Mutual Information Neural-Estimation-Driven Constellation Shaping Design and Performance Analysis.

Xiuli Ji1, Qian Wang1, Liping Qian1

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Summary
This summary is machine-generated.

Novel deep learning techniques using mutual information neural estimation (MINE) optimize constellation shaping for high-speed communications. These methods enhance mutual information (MI) performance, offering a simpler, more effective approach for wireless and optical systems.

Keywords:
constellation shapinghigh-order QAMmutual information maximizationmutual information neural estimation

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Area of Science:

  • Optical and Wireless Communications
  • Information Theory
  • Deep Learning Applications

Background:

  • Constellation design significantly impacts wireless and optical communication performance.
  • High-order modulations in high-speed systems necessitate advanced constellation shaping for increased capacity.
  • Traditional shaping methods often involve complex computational and structural requirements.

Purpose of the Study:

  • To propose novel mutual information neural estimation (MINE)-based geometric, probabilistic, and joint constellation shaping schemes (MINE-GCS, MINE-PCS, MINE-JCS).
  • To maximize mutual information (MI) in high-speed coherent communication systems using deep learning (DL).
  • To develop a transmitter-side optimization approach that avoids the complexity of traditional shaping techniques.

Main Methods:

  • Introduction of a MINE module to estimate and maximize MI via backpropagation, independent of channel state information.
  • Training of encoder and probability generator networks across various signal-to-noise ratios to optimize constellation point distribution and probabilities.
  • Transformation of MI calculation into a parameter optimization problem using MINE.

Main Results:

  • MINE-based schemes demonstrate superior MI performance compared to unshaped Quadrature Amplitude Modulation (QAM) and other DL-based joint shaping schemes.
  • MINE-JCS achieved the best performance under additive white Gaussian noise.
  • A low-order 8-ary MINE-GCS showed an MI gain of approximately 0.1 bits/symbol over unshaped Star-8QAM.

Conclusions:

  • The proposed MINE-based constellation shaping schemes effectively maximize mutual information.
  • These methods offer a balance between implementation complexity and MI performance.
  • The schemes are adaptable for practical applications across diverse noise and fading conditions.