Short Codes with Near-ML Universal Decoding: Are Random Codes Good Enough?

Abstract

Short blocklength codes have an important role in machine-type and ultra-low-latency communications. Unfortunately, reducing the blocklength makes it very challenging to achieve good error-correcting performance. There exist near-ML decoding algorithms with manageable complexity for short blocklength codes, such as ordered statistics decoding and the more recent guessing random additive noise decoding algorithm. These algorithms have the additional advantage that they are universal, in the sense that they can decode any linear block code. For this reason, some recent works have attempted to construct unstructured linear codes for use with universal decoders using sophisticated techniques, such as reinforcement learning. In this work, we first describe a genetic-algorithm-aided (GA-aided) construction method for unstructured codes and we then compare a very simple random construction to both the GA-aided construction and the reinforcement learning construction. Our simulation results indicate that, while some care should be taken when selecting an unstructured code, sophisticated and complex code construction methods may not be necessary in the sense that they lead to minimal improvements.