Turbo Codes#

TurboEncoder([gen_poly, constraint_length, ...])

Performs encoding of information bits to a Turbo code codeword.

TurboDecoder([encoder, gen_poly, rate, ...])

Turbo code decoder based on BCJR component decoders [Berrou].

This module supports encoding and decoding of Turbo codes [Berrou], e.g., as used in the LTE wireless standard. The convolutional component encoders and decoders are composed of the ConvEncoder and BCJRDecoder layers, respectively.

Please note that various notations are used in literature to represent the generator polynomials for the underlying convolutional codes. For simplicity, TurboEncoder only accepts the binary format, i.e., 10011, for the generator polynomial which corresponds to the polynomial \(1 + D^3 + D^4\).

The following code snippet shows how to set-up a rate-1/3, constraint-length-4 TurboEncoder and the corresponding TurboDecoder. You can find further examples in the Channel Coding Tutorial Notebook.

Setting-up:

encoder = TurboEncoder(constraint_length=4, # Desired constraint length of the polynomials
                       rate=1/3,  # Desired rate of Turbo code
                       terminate=True) # Terminate the constituent convolutional encoders to all-zero state
# or
encoder = TurboEncoder(gen_poly=gen_poly, # Generator polynomials to use in the underlying convolutional encoders
                       rate=1/3, # Rate of the desired Turbo code
                       terminate=False) # Do not terminate the constituent convolutional encoders

# the decoder can be initialized with a reference to the encoder
decoder = TurboDecoder(encoder,
                       num_iter=6, # Number of iterations between component BCJR decoders
                       algorithm="map", # can be also "maxlog"
                       hard_out=True) # hard_decide output

Running the encoder / decoder:

# --- encoder ---
# u contains the information bits to be encoded and has shape [...,k].
# c contains the turbo encoded codewords and has shape [...,n], where n=k/rate when terminate is False.
c = encoder(u)

# --- decoder ---
# llr contains the log-likelihood ratio values from the de-mapper and has shape [...,n].
# u_hat contains the estimated information bits and has shape [...,k].
u_hat = decoder(llr)

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