Joint Transmission and Control in a Goal-oriented NOMA Network
Abstract
Goal-oriented communication shifts the focus from merely delivering timely information to maximizing decision-making effectiveness by prioritizing the transmission of high-value information. In this context, we introduce the Goal-oriented Tensor (GoT), a novel closed-loop metric designed to directly quantify the ultimate utility in Goal-oriented systems, capturing how effectively the transmitted information meets the underlying application's objectives. Leveraging the GoT, we model a Goal-oriented Non-Orthogonal Multiple Access (NOMA) network comprising multiple transmission-control loops. Operating under a pull-based framework, we formulate the joint optimization of transmission and control as a Partially Observable Markov Decision Process (POMDP), which we solve by deriving the belief state and training a Double-Dueling Deep Q-Network (D3QN). This framework enables adaptive decision-making for power allocation and control actions. Simulation results reveal a fundamental trade-off between transmission efficiency and control fidelity. Additionally, the superior utility of NOMA over Orthogonal Multiple Access (OMA) in multi-loop remote control scenarios is demonstrated.
Cite
@article{arxiv.2503.13873,
title = {Joint Transmission and Control in a Goal-oriented NOMA Network},
author = {Kunpeng Liu and Shaohua Wu and Aimin Li and Qinyu Zhang},
journal= {arXiv preprint arXiv:2503.13873},
year = {2025}
}