Iterative decoding of concatenated codes: a tutorial
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Bit error rate estimation for turbo decoding
IEEE Transactions on Communications
On the performance of space-time coded and spatially multiplexed MIMO systems with linear receivers
IEEE Transactions on Communications
Achievable rates of coded linear systems with iterative MMSE detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Belief propagation, Dykstra's algorithm, and iterated information projections
IEEE Transactions on Information Theory
Semi-analytical performance prediction methods for iterative MMSE-IC multiuser MIMO joint decoding
IEEE Transactions on Communications
Soft-chip combining MIMO multicarrier CDMA antijam system
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
On MSE Exit Chart Analysis and Real Time DSP Implementation for Iterative (Turbo) Detection
Journal of Signal Processing Systems
Hi-index | 754.90 |
We introduce a simple technique for analyzing the iterative decoder that is broadly applicable to different classes of codes defined over graphs in certain fading as well as additive white Gaussian noise (AWGN) channels. The technique is based on the observation that the extrinsic information from constituent maximum a posteriori (MAP) decoders is well approximated by Gaussian random variables when the inputs to the decoders are Gaussian. The independent Gaussian model implies the existence of an iterative decoder threshold that statistically characterizes the convergence of the iterative decoder. Specifically, the iterative decoder converges to zero probability of error as the number of iterations increases if and only if the channel E b/N0 exceeds the threshold. Despite the idealization of the model and the simplicity of the analysis technique, the predicted threshold values are in excellent agreement with the waterfall regions observed experimentally in the literature when the codeword lengths are large. Examples are given for parallel concatenated convolutional codes, serially concatenated convolutional codes, and the generalized low-density parity-check (LDPC) codes of Gallager and Cheng-McEliece (1996). Convergence-based design of asymmetric parallel concatenated convolutional codes (PCCC) is also discussed