In digital communication systems, Forward Error Correction (FEC) techniques are employed to improve the bit error rate during data transmission over unreliable communication channels. FEC introduces redundancy, allowing the receiver to utilize this additional information during the error correction stage. The decoding algorithms leverage a probabilistic approach based on soft demodulation of the received symbols into bit log-likelihood ratios (LLRs), which provide a measure of the likelihood of different bits given the received signal. However, calculating LLRs becomes computationally expensive when dealing with large constellations and, in non-ideal Gaussian channels, it is difficult to accurately calculate them. In this work, we present various approaches for LLR computation in non-Gaussian channels and compare traditional algorithm-based equalization schemes with machine-learning techniques, employing soft demodulators to generate inputs for a low-density parity-check decoder.