mokka.inft
Module implementing functions in information theory for MOKKa.
Implementation of information theoretic functions in pyTorch for MOKKa.
- mokka.inft.torch.BMI(m, N, b_ij, L_ij, p=None)
Calculate the bitwise mutual information.
Originally written by Benedikt Geiger
Parameters
- mint
number of bits
- Nint
number of sent/received symbols
- b_ij2D-matrix_like
value of the i-th (Sent) modulation symbol at the j-th position in the bit-string
- L_ij2D-matrix_like
LLRs
- p1D-matrix_like
probability of occurence of the sent symbols to adjust source entropy.
Returns
- BMIfloat
bitwise mutual information
- mokka.inft.torch.MI(M, PX, N, symbol_idx, Q_YX)
Calculate the mutual information.
Numerically Computing Achievable Rates of Memoryless Channels
Parameters
- Mint
number of symbols
- Nint
number of sent/received symbols
- Q_YX2D-matrix_like
N x M conditonal probabilities
- PX2D-matrix_like
1 x M
- symbol_idx1D-matrix_like
sent symbol indices
Returns
- MIfloat
mutual information
- mokka.inft.torch.SNR(M, sym_idx, rx_syms)
Calculate the signal to noise ratio.
Parameters
- Mint
number of constellation points
- sym_idxVector
Transmitted symbol indices between 0 and M-1
- rx_symsVector
Complex received symbols
Returns
- SNRfloat
Signal to noise ratio in linear units
- SNR_dB :float
Signal to noise ratio in decibels
- mokka.inft.torch.hMI(m, N, symbol_idx, demapped_symbol_idx)
Calculate the mutual information according to “Achievable Rates for Probabilistic Shaping” (Eq. 87).
If probabilistic shaping is applied this has to be reflected in the transmit symbols indices
Parameters
- mint
number of bits per symbol
- Nint
number of sent/received symbols
- symbol_idx1D-matrix_like
sent symbol indices
- demapped_symbol_idx1D-matrix_like
demapped symbol indices
Returns
- BMIfloat
bitwise mutual information