Contents
What is the constraint length for Viterbi decoder?
It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J. Viterbi and published in the paper Viterbi, A. (April 1967). “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm”.
How is the code rate defined in the Viterbi algorithm?
The convolutional code used by the Viterbi algorithm is defined by two parameters: code rate and constraint length. The code rate, R=k/n, is expressed as a ratio of the number of input bits into the convolutional encoder (k) to the number of channel symbols output by the convolutional encoder (n) in a given encoder cycle.
When to use convolutional encoding with Viterbi decoding?
Convolutional encoding with Viterbi decoding is a FEC technique that is well suited for use with channels where the transmitted signal is corrupted mainly by Additive White Gaussian Noise (AWGN). The convolutional encoder inserts redundant information bits into the data stream so that the decoder can reduce and correct errors caused by the channel.
What’s the general approach to traceback in Viterbi?
The general approach to traceback is to accumulate path metrics for up to five times the constraint length (5 ( K – 1)), find the node with the largest accumulated cost, and begin traceback from this node.
Which is the best decoder for convolutional codes?
Viterbi decoder. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J. Viterbi and published in the paper “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm”.
What is the optimal quantization zone width for Viterbi?
The optimal quantization zone width is defined by the following formula: is a noise power spectral density, and k is a number of bits for soft decision. ) distance between the received and the actual symbols in the code alphabet may be further simplified into a linear sum/difference form, which makes it less computationally intensive.
Which is the best decoder for maximum likelihood?
The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It is most often used for decoding convolutional codes with constraint lengths k<=3, but values up to k=15 are used in practice.