Semantic Communications Based on Adaptive Generative Models and Information Bottleneck

TL;DR

This paper introduces a semantic communication framework that leverages adaptive generative models and the information bottleneck principle to improve transmission efficiency by focusing on meaning rather than exact symbol recovery.

Contribution

It proposes a novel semantic communication approach combining topological data representation, adaptive information bottleneck, and probabilistic generative models for efficient wireless transmission.

Findings

Enhanced transmission efficiency by focusing on semantic content

Adaptive information bottleneck improves trade-offs between power, accuracy, and delay

Probabilistic generative models enable flexible rate adaptation and data regeneration

Abstract

Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In semantic communications, the scope of the destination is not to recover a list of symbols symbolically identical to the transmitted ones, but rather to recover a message that is semantically equivalent to the semantic message emitted by the source. This paradigm shift introduces many degrees of freedom to the encoding and decoding rules that can be exploited to make the design of communication systems much more efficient. In this paper, we present an approach to semantic communication building on three fundamental ideas: 1) represent data over a topological space as a formal way to capture semantics, as expressed through relations; 2) use the information bottleneck principle as a way to identify relevant information and adapt the information bottleneck online, as a function of the wireless channel state, in order to strike an optimal trade-off between transmit power, reconstruction accuracy and delay; 3) exploit probabilistic generative models as a general tool to adapt the transmission rate to the wireless channel state and make possible the regeneration of the transmitted images or run classification tasks at the receiver side.