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.

翻译：语义通信在为下一代无线系统提供面向目标的服务方面将发挥关键作用,然而,这一领域大多数先前的艺术都局限于特定应用(如文字或图像),无法进行面向目标的通信,在这种通信中,传递的信息的效力必须与语义通信一起考虑,以便执行某一任务。在本文件中,提议了一个全面的语义通信框架,以便开展面向目标的任务执行。为了捕捉演讲者和听众之间的语义,使用信仰概念定义了一种共同的语言,使演讲者能够向听众描述环境观测。然后,出现了一个优化问题,即选择一套最起码的信仰,在尽可能缩短任务执行时间和传输成本的同时,能够完美地描述观测结果。提出了一个新的自上至下框架,将课程学习(CL)与强化学习(RL)结合起来,以解决该问题。模拟结果表明,拟议的CL方法在聚合时间、任务执行时间和培训期间的传输成本方面超越了传统的RL。