Delta Optimization for Event-Based Sampling in Goal-Oriented Communication Systems

Goal-oriented communication (GOC) has emerged as a promising paradigm in modern wireless systems, emphasizing transmitting information relevant to the goal rather than the raw data itself. This study investigates event-based sampling methods, specifically the Send-on-Delta (SOD) and Send-on-Delta with Linear Prediction (SODwLP) algorithms, to explore the trade-off between normalized mean square error (NMSE) and the number of samples (NoS) in resourceconstrained scenarios. While SOD achieves fewer transmitted samples compared to SODwLP, it exhibits higher NMSE, making it less favorable in terms of signal reconstruction accuracy. Through a grid search optimization strategy, we demonstrated that SODwLP achieves a lower overall cost at equivalent delta values and consistently selects larger optimal delta thresholds across various weight configurations, balancing communication efficiency and accuracy more effectively. These results highlight the adaptability of SODwLP for GOC systems, where specific priorities such as accuracy or energy efficiency can significantly influence the choice of sampling parameters. Future research could extend these findings by employing advanced optimization techniques and evaluating real-world applications in IoT and 6G networks to enhance the practical relevance of these algorithms.