Title: Dynamic Relative Representations for Goal-Oriented Semantic Communications Show Chinese title

Title: 动态相对表示在目标导向语义通信中的应用

Abstract: 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. Show Chinese abstract

Abstract: 在未来6G无线网络中，通信的意义和有效性方面将发挥根本性作用，将意义和相关性融入传输。 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，导致语义不匹配，可能危及理解。 在潜在空间通信中，这一挑战表现为高维表示中的不对齐，在此深神经网络编码数据。 本文提出了一种面向目标的语义通信的新框架，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，适应相对表示、通信参数和计算资源，以实现能量高效、低延迟、面向目标的语义通信。 数值结果证明了我们的方法在缓解设备间不匹配方面的有效性，同时优化了能量消耗、延迟和有效性。