6G goal-oriented communications: How to coexist with legacy systems?

TL;DR

This paper explores spectrum sharing between legacy 6G communication systems and new goal-oriented edge intelligence, focusing on optimizing data rate and inference confidence through a novel goal-based framework and Lyapunov optimization.

Contribution

It introduces a formal definition of goals in wireless communications, analyzes the link between reliability and goal-effectiveness, and proposes an optimization strategy for coexistence.

Findings

Goal-oriented strategy outperforms legacy approaches.

Reliability and goal-effectiveness are not directly correlated.

Optimization guarantees desired performance in various environments.

Abstract

6G will connect heterogeneous intelligent agents to make them operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how AI and ML models behave depending on: i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper we investigate how to effectively share spectrum resources between a legacy communication system and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an eMBB service, i.e., a user uploading a video stream, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal-effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal-effectiveness, as well as that of goal cost . We argue and show, through numerical evaluations, that communication reliability and goal-effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal-effectiveness, a long-term optimization problem is formulated and solved via Lyapunov optimization tools, to guarantee the desired performance. Finally, our numerical results assess the advantages of the proposed optimization, and the superiority of the goal-oriented strategy against baseline 5G compliant legacy approaches, under both stationary and non-stationary environments.