Goal-oriented Tensor: Beyond Age of Information Towards Semantics-Empowered Goal-Oriented Communications

TL;DR

This paper introduces the Goal-oriented Tensor (GoT), a new metric for semantic-aware, goal-driven communication systems, and develops a co-design framework for sampling and decision-making that outperforms traditional Age of Information metrics.

Contribution

It proposes the Goal-oriented Tensor (GoT) as a novel metric and formulates a decentralized decision process, along with an efficient algorithm for goal-oriented communication optimization.

Findings

GoT effectively quantifies semantic mismatches impacting decision utility.

The co-designed sampler and decision-maker outperform AoI-based methods in simulations.

The proposed approach achieves higher goal utility with sparser sampling rates.

Abstract

Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system's decision-making utility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-making utility. Leveraging the GoT, we consider a sampler & decision-maker pair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent the curse of dimensionality in obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposed sampler & decision-maker co-design surpasses the current literature on AoI and its variants in terms of both goal achievement utility and sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.