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.

在未来的 6G 无线网络中，语义和效能方面的通信将起到基础作用，将意义和相关性融入传输中。本文提出了一个新的面向目标的语义通信框架，通过潜在空间对齐来减轻语义不匹配问题。我们提出了一种动态优化策略，适应相对表示、通信参数和计算资源，实现了高效能、低延迟、面向目标的能源储效率的语义通信。数值结果表明我们的方法在减轻设备间不匹配问题的同时，优化了能源消耗、延迟和效能。