Distribution Channel Encoding with Polarization Streams for Correlated Data

The patent transforms the encoding approach by changing from treating information bits as independent scalar values to representing them as correlated probability distributions with multiple parameters (mean, variance, and potentially higher-order moments). This parameter change allows the encoder to capture correlations between information bits while maintaining computational tractability through distributional representations.

The invention extends the encoding space from scalar bit values to multi-dimensional probability distribution spaces. By representing each information bit as a distribution characterized by multiple parameters rather than a single value, the system adds dimensional complexity that enables capturing correlations and improving transmission reliability without sacrificing efficiency.

The patent replaces traditional mechanical or algorithmic encoding mechanisms with a neural network-based distribution channel encoder. This substitution allows the complex task of generating dimension-extended codewords while preserving correlations to be learned and executed automatically, reducing the apparent complexity of the encoding scheme while maintaining high encoding accuracy.

The distribution channel encoder is designed to perform multiple functions simultaneously: it extends the dimensionality of codewords, preserves correlations between information bits, and generates probability distributions that can be directly used for reliable transmission. This multi-functionality reduces the need for separate processing stages, thereby managing complexity while achieving high encoding accuracy.