  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.
