以目标为导向的语义交流的共同语言——一个课程学习框架

语义通信将在下一代无线系统上实现面向目标的服务中发挥关键作用。然而，该领域的大多数现有技术仅限于特定的应用（如文本或图像），它不能实现面向目标的通信，在这种通信中，必须考虑传输信息的有效性和语义，以便执行某种任务。本文提出了一个全面的语义通信框架，以实现面向目标的任务执行。为了捕捉说话人和听话人之间的语义，使用信念的概念定义了一种共同语言，使说话人能够向听话人描述环境观察。然后，提出了一个优化问题，以选择能完美描述观察结果的最小信念集，同时使任务执行时间和传输成本最小。一个新颖的自上而下的框架，结合了课程学习（CL）和强化学习（RL），被提出来解决这个问题。仿真结果表明，所提出的课程学习方法在训练期间的收敛时间、任务执行时间和传输成本方面优于传统的RL。

原文题目：Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

原文：Semantic communications will play a critical role in enabling goal-oriented services over next-generation wireless systems. However, most prior art in this domain is restricted to specific applications (e.g., text or image), and it does not enable goal-oriented communications in which the effectiveness of the transmitted information must be considered along with the semantics so as to execute a certain task. In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. To capture the semantics between a speaker and a listener, a common language is defined using the concept of beliefs to enable the speaker to describe the environment observations to the listener. Then, an optimization problem is posed to choose the minimum set of beliefs that perfectly describes the observation while minimizing the task execution time and transmission cost. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.