Dynamic Relative Representations for Goal-Oriented Semantic
  Communications
Simone Fiorellino Claudio Battiloro Emilio Calvanese Strinati Paolo Di Lorenzo
In future 6G wireless networks, semantic and effectiveness aspects of
communications will play a fundamental role, incorporating meaning and
relevance into transmissions. However, obstacles arise when devices employ
diverse languages, logic, or internal representations, leading to semantic
mismatches that might jeopardize understanding. In latent space communication,
this challenge manifests as misalignment within high-dimensional
representations where deep neural networks encode data. This paper presents a
novel framework for goal-oriented semantic communication, leveraging relative
representations to mitigate semantic mismatches via latent space alignment. We
propose a dynamic optimization strategy that adapts relative representations,
communication parameters, and computation resources for energy-efficient,
low-latency, goal-oriented semantic communications. Numerical results
demonstrate our methodology's effectiveness in mitigating mismatches among
devices, while optimizing energy consumption, delay, and effectiveness.