  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.
