Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers

The preservation of relevant mutual information under compression is the fundamental challenge of the information bottleneck method. It has many applications in machine learning and in communications. The recent literature describes successful applications of this concept in quantized detection and channel decoding schemes. The focal idea is to build receiver algorithms intended to preserve the maximum possible amount of relevant information, despite very coarse quantization. The existent literature shows that the resulting quantized receiver algorithms can achieve performance very close to that of conventional high-precision systems. Moreover, all demanding signal processing operations get replaced with lookup operations in the considered system design. In this paper, we develop the idea of maximizing the preserved relevant information in communication receivers further by considering parametrized systems. Such systems can help overcome the need of lookup tables in cases where their huge sizes make them impractical. We propose to apply genetic algorithms which are inspired from the natural evolution of the species for the problem of parameter optimization. We exemplarily investigate receiver-sided channel output quantization and demodulation to illustrate the notable performance and the flexibility of the proposed concept.

Automated summary

The information bottleneck method aims to preserve as much relevant mutual information as possible under compression, with applications in various fields like communication and machine learning. By considering parameterized systems, the efficiency of preserving relevant information in communication receivers can be further improved.