a general and flexible channel decoding approach based on probabilistic programming language

Channel coding is a vital component within digital telecommunications, helping to deal with unwanted factors like noise and enabling the establishment of a more robust communication link. among the most renowned coding schemes are linear block codes, for which a variety of decoding methods have been proposed in recent years. this paper demonstrates how a linear block coding problem can be expressed as a probabilistic graphical model (pgm). we then explain how probabilistic programming languages (ppls), which are tools for solving such pgms, can be used to decode this type of coding. employing the figaro programming language, as a ppl, we have simulated the decoding of several famous linear block codes and found that the results of our proposed method closely match those of existing techniques. our approach offers several advantages, such as the flexibility to utilize diverse inference methods, the ability to choose between hard and soft decoding dynamically, and the implementation of a wide range of coding techniques. ppls also enable the adjustment of decoding algorithm parameters and the estimation of channel conditions, ultimately enhancing the receiver's adaptability to varying channel conditions. finally, we discuss the advantages and disadvantages of our proposed method.