Goal-Oriented Tensor: Beyond Age of Information Toward Semantics-Empowered Goal-Oriented Communications
Aimin Li, Shaohua Wu, Sumei Sun, Jie Ming Cao · IEEE Transactions on Communications · 2024
Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system’s decision-makingutility. 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-makingutility. Leveraging the GoT, we consider asampler & decision-makerpair 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 thecurse of dimensionalityin 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 proposedsampler & decision-makerco-design surpasses the current literature on AoI and its variants in terms of both goal achievementutilityand sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.