标题： 面向目标的语义通信的动态相对表示 显示英文标题

标题： Dynamic Relative Representations for Goal-Oriented Semantic Communications

摘要： 在未来6G无线网络中，通信的意义和有效性方面将发挥基础性作用，将意义和相关性融入传输中。 然而，当设备使用不同的语言、逻辑或内部表示时，障碍就会出现，导致可能危及理解的语义不匹配。 在潜在空间通信中，这一挑战表现为深度神经网络编码数据的高维表示中的错位现象。 本文提出了一种面向目标的语义通信新框架，利用相对表示通过潜在空间对齐来缓解语义不匹配。 我们提出了一种动态优化策略，该策略根据能量效率、低延迟和面向目标的语义通信需求，调整相对表示、通信参数和计算资源。 数值结果表明，我们的方法在减少设备之间的不匹配的同时，还优化了能耗、延迟和有效性。 显示英文摘要

摘要： 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.