Acceleration of the Finite-Element Gaussian Belief Propagation Solver Using Minimum Residual Techniques

Abstract

The finite-element Gaussian belief propagation (FGaBP) method, introduced recently, provides a powerful alternative to the conventional finite-element method solvers to efficiently utilize high-performance computing platforms. In this paper, we accelerate the FGaBP convergence by combining it with two methods based on residual minimization techniques, namely, the flexible generalized minimum residual and the iterant recombination method. The numerical results show considerable reductions in the total number of operations compared with the stand-alone FGaBP method, while maintaining the scalability features of FGaBP.Index Terms-Finite-element method (FEM), Gaussian belief propagation (GaBP), iterative methods, Krylov subspace methods.

Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network

“…The symbol L i,j in the figure represents the update of the information node to the left; R i,j indicates the update of the information node to the right. One iteration of the BP decoding algorithm is that the information is first transmitted from the rightmost node to the leftmost node and then from the leftmost node to the rightmost node [32,33]. In this process, the transmission of information is expressed in the form of LLR.…”