An Intuitive Justification and a Simplified Implementation of the MAP Decoder for Convolutional Codes

By A. Viterbi. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Spatially Transformed Adversarial Examples (2018, background): “This observation conﬁrms the observation that when DNNs extract edge information in the earlier layers for visual recognition tasks (Viterbi, 1998).”
Turbo Coding, Turbo Equalisation and Space-Time Coding (2002, methodology): “Viterbi noted furthermore [129, 130] that the Max-Log-MAP algorithm can be viewed as two generalised Viterbi decoders (one for the forward recursion, and one for the backward recursion) together with a generalised dualmaxima computation (for calculating the soft outputs using Equation 4.”
Efficient implementations of the sum-product algorithm for decoding LDPC codes (2001, methodology): “The first version is analogous to the h operation used in turbo codes [9, 10].”
Turbo Coding, Turbo Equalisation and Space-Time Coding for Transmission over Fading Channels (2002, methodology): “Viterbi noted furthermore [130, 131] that the Max-Log-MAP algorithm can be viewed as two generalised Viterbi decoders (one for the forward recursion, and one for the backward recursion) together with a generalised dual-maxima computation (for calculating the soft outputs using Equation 5.”
Replacing the Soft-Decision FEC Limit Paradigm in the Design of Optical Communication Systems (2015, background): “To alleviate the computational complexity of (6), the wellknown max-log approximation [42]”
Turbo equalization: adaptive equalization and channel decoding jointly optimized (2001, methodology): “This algorithm is less optimum than other algorithms but gives good performance with reasonable complexity requirements [15].”
On the performance of hybrid FEC/ARQ systems using rate compatible punctured turbo (RCPT) codes (2000, background): “In [ 24 ], it is claimed that the processing load in the log-MAP decoder is bounded at no more than four”
Comparative study of turbo decoding techniques: an overview (2000, unknown): “However, Viterbi also states [27], [28] that by increasing the computational load slightly, to four times that of the Viterbi algorithm, the memory requirements can be dramatically reduced to become essentially equal to those of the Viterbi decoder.”