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.

翻译：在未来的6G无线网络中，通信的语义与有效性层面将发挥基础性作用，将含义与相关性融入传输过程。然而，当设备采用不同语言、逻辑或内部表示时，会出现语义不匹配问题，可能危及理解。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之间的不对齐。本文提出了一种新颖的面向目标语义通信框架，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，能够自适应调整相对表示、通信参数和计算资源，以实现高能效、低延迟的面向目标语义通信。数值结果表明，所提方法在缓解设备间不匹配问题的同时，能够有效优化能耗、延迟与通信有效性。