Gradient Descent Bit-Flipping Decoding with Momentum
Valentin Savin
Abstract
In this paper, we propose a Gradient Descent Bit-Flipping (GDBF) decoding with momentum, which considers past updates to provide inertia to the decoding process. We show that GDBF or randomized GDBF decoders with momentum may closely approach the floating-point Belief-Propagation decoding performance, and even outperform it in the error-floor region, especially for graphs with high connectivity degree.
Topics & Concepts
Decoding methodsBit (key)Computer scienceGradient descentMomentum (technical analysis)AlgorithmInertiaPoint (geometry)Theoretical computer scienceMathematicsArtificial intelligencePhysicsQuantum mechanicsArtificial neural networkFinanceComputer securityEconomicsGeometryError Correcting Code TechniquesCooperative Communication and Network CodingAdvanced Data Storage Technologies