A modified Gradient Descent Bit Flipping (GDBF) algorithm is proposed for decoding Low Density Parity Check (LDPC) codes on the binary-input additive white Gaussian noise channel. The new algorithm, called Noisy GDBF (NGDBF), introduces a random perturbation into each symbol metric at each iteration. The noise perturbation allows the algorithm to escape from undesirable local maxima, resulting in improved performance. A combination of heuristic improvements to the algorithm are proposed and evaluated. When the proposed heuristics are applied, NGDBF performs better than any previously reported GDBF variant, and comes within 0.5 dB of the belief propagation algorithm for several tested codes. Unlike other previous GDBF algorithms that provide an escape from local maxima, the proposed algorithm uses only local, fully parallelizable operations and does not require computing a global objective function or a sort over symbol metrics, making it highly efficient in comparison. The proposed NGDBF algorithm requires channel state information which must be obtained from a signal to noise ratio (SNR) estimator. Architectural details are presented for implementing the NGDBF algorithm. Complexity analysis and optimizations are also discussed.
To reduce the arithmetic complexity of bit-flipping algorithms, devised the GDBF algorithm as a gradient-descent optimization model for the ML decoding problem @cite_5 . Based on this model, the authors of @cite_5 obtained single-bit and multi-bit flipping algorithms that require mainly binary operations, similar to the original BFA. The GDBF methods require summation of binary parity-check values, which is less complex than the WBF algorithms that require summation over independently weighted syndrome values. The single-bit version of the GDBF algorithm (S-GDBF) requires a global search to discover the least reliable bit at each iteration. The multi-bit GDBF algorithm (M-GDBF) uses local threshold operations instead of a global search, hence achieving a faster initial convergence. In practice, the M-GDBF algorithm did not always provide stable convergence to the final solution. To improve convergence, the authors of @cite_5 adopted a mode-switching strategy in which M-GDBF decoding is always followed by a phase of S-GDBF decoding, leveraging high-speed in the first phase and accurate convergence in the second.
@cite_5
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A novel class of bit-flipping (BF) algorithm for decoding low-density parity-check (LDPC) codes is presented. The proposed algorithms, which are referred to as gradient descent bit flipping (GDBF) algorithms, can be regarded as simplified gradient descent algorithms. The proposed algorithms exhibit better decoding performance than known BF algorithms, such as the weighted BF algorithm or the modified weighted BF algorithm for several LDPC codes.