Practical channel coding methods for channels with input-dependent noise

With the emergence of new applications and needs, communications theory started to be applied to outside of the traditional radio frequency (RF) bands or even in completely different channels. Molecular communication and visible light communication (VLC) are examples of such emerging use cases that communication takes place in a specific channel. Not surprisingly, those channels bring their own challenges and differences compared to the conventional wireless channels. One common point among many of those new channels is that the noise depends on the input signal. This situation is contrary to the prevalent assumption of existence of white noise in the design of wireless communication blocks. Since white noise assumption is not valid, applying directly conventional methods to the new channels yields unsatisfactory performances. In this thesis work, our aim was to develop practical channel coding methods for the channels with input dependent noise. For molecular communication via diffusion (MCvD), we propose 2 novel decoding methods coupled with constant low weight codes. Iterative sorting decoder is a decision-feedback heuristic method that iteratively calculates the intersymbol interference (ISI) from a better estimation at each step. The second proposed method is the super trellis decoder, which is a maximum a posteriori sequence estimator. Iterative sorting and super trellis decoders bring substantially better performances than the existing methods. For VLC, a deep learning based VLCnet is proposed. VLCnet has a novel activation unit, FRAU, to achieve flicker reduction and dimming, which are two main illumination needs. By allowing joint optimization of both the encoder and decoder, VLCnet performs superior compared to the other proposed techniques in the literature.