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6G无线网络中，通信的语义与有效性层面将发挥根本性作用，将意义与关联性融入传输过程。然而，当设备采用不同的语言、逻辑或内部表示时，障碍随之产生，导致可能危及理解的语义失配。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所在的高维表示空间内的未对齐问题。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失配。我们提出了一种动态优化策略，该策略自适应地调整相对表示、通信参数与计算资源，以实现高能效、低延迟的面向目标语义通信。数值结果证明了我们的方法在优化能耗、延迟与有效性的同时，能够有效缓解设备间的语义失配。