Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

• URL: http://arxiv.org/abs/2503.06725v1
• Date: Sun, 09 Mar 2025 18:51:14 GMT
• Title: Pull-Based Query Scheduling for Goal-Oriented Semantic Communication
• Authors: Pouya Agheli, Nikolaos Pappas, Marios Kountouris,
• Abstract summary: This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems.<n>We introduce a grade of effectiveness (GoE) metric and integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis.<n>We propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms.
• Score: 14.787190731074322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce a grade of effectiveness (GoE) metric. Furthermore, we integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness.

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