Relative Entropy based Message Combining for Exploiting Diversity in Information Optimized Processing

Maximizing information has become a powerful technique for the design of efficient receiver components with very low bit resolution. In the established information processing approach, the algorithmic tasks are executed on discrete messages and the processing steps are designed to optimize the mutual information. In this paper, we extend the concept of information optimized processing for exploiting diversity. To that end, we propose the Relative Entropy based Message Combining (REMC) approach in order to merge discrete messages with different underlying distributions, e.g., stemming from different diversity branches. We exemplary evaluate the proposed REMC for a single-user uplink-model with multiple Radio Access Points (RAPs) and higher order modulation schemes. The numerical results show that message combining is required to leverages diversity gains in an information optimized receiver.