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Generalized Adaptive Diversity Gradient Descent Bit-Flipping with a Finite State Machine.

Jovan Milojković1, Srdjan Brkić2, Predrag Ivaniš1

  • 1School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.

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Summary
This summary is machine-generated.

We developed a new gradient descent bit-flipping algorithm with a finite state machine (GDBF-wSM) for decoding low-density parity-check (LDPC) codes, improving performance over existing methods.

Keywords:
bit-flipping algorithmfinite state machinegradient descentiterative decodinglow-density parity-check codesmomentum

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Area of Science:

  • Information Theory
  • Coding Theory
  • Digital Communications

Background:

  • Low-density parity-check (LDPC) codes are crucial for reliable data transmission in modern communication systems.
  • Iterative decoding algorithms are essential for achieving the near-capacity performance of LDPC codes.
  • Existing gradient descent bit-flipping algorithms can be further optimized for improved decoding efficiency.

Purpose of the Study:

  • To introduce a novel gradient descent bit-flipping algorithm with a finite state machine (GDBF-wSM) for LDPC code decoding.
  • To develop a learnable framework for optimizing decoder parameters using error pattern databases.
  • To evaluate the performance of the proposed GDBF-wSM algorithm in various channel conditions.

Main Methods:

  • Implementation of a finite state machine (FSM) to dynamically adjust variable node update values during decoding.
  • Development of a parameter optimization framework leveraging a database of uncorrectable error patterns.
  • Performance evaluation using regular LDPC codes over Binary Symmetric Channel (BSC) and Additive White Gaussian Noise (AWGN) channels.

Main Results:

  • The proposed GDBF-wSM algorithm demonstrates improved performance compared to existing GDBF-based approaches.
  • The finite state machine effectively adapts node updates based on previous flipping behavior.
  • The learnable framework shows potential for further decoder parameter optimization.

Conclusions:

  • The GDBF-wSM algorithm offers a promising advancement in iterative decoding of LDPC codes.
  • The integration of finite state machines enhances decoding adaptability and performance.
  • The proposed learnable framework provides a pathway for systematic decoder optimization.