  This paper addresses the prediction of error floors of low-density
parity-check (LDPC) codes with variable nodes of constant degree in the
additive white Gaussian noise (AWGN) channel. Specifically, we focus on the
performance of the sum-product algorithm (SPA) decoder formulated in the
log-likelihood ratio (LLR) domain. We hypothesize that several published error
floor levels are due to the manner in which decoder implementations handled the
LLRs at high SNRs. We employ an LLR-domain SPA decoder that does not saturate
near-certain messages and find the error rates of our decoder to be lower by at
least several orders of magnitude. We study the behavior of trapping sets (or
near-codewords) that are the dominant cause of the reported error floors.
  We develop a refined linear model, based on the work of Sun and others, that
accurately predicts error floors caused by elementary tapping sets for
saturating decoders. Performance results of several codes at several levels of
decoder saturation are presented.
