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

TL;DR

This paper introduces a goal-oriented semantic communication framework using a common language based on beliefs, optimized through a curriculum learning approach to improve task execution efficiency and reduce transmission costs.

Contribution

It proposes a novel semantic communication framework with a belief-based common language and a curriculum learning method to optimize task execution and communication efficiency.

Findings

Curriculum learning outperforms traditional reinforcement learning in convergence speed.

The framework reduces task execution time and transmission costs.

Simulation results validate the effectiveness of the proposed approach.

Abstract

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.