Joint Transmission and Control in a Goal-oriented NOMA Network

2025-03-18

AI Summary

In goal-oriented communication for NOMA-based remote control networks, tight coupling between information transmission and control decision-making fundamentally limits task-level utility. Method: We propose a joint optimization framework comprising: (i) a transmission–control co-design model formulated as a partially observable Markov decision process (POMDP); (ii) a novel “Goal-oriented Tensor” (GoT) as a closed-loop, task-level utility metric; (iii) theoretical analysis revealing the intrinsic trade-off between transmission efficiency and control fidelity under NOMA; and (iv) a pull-based closed-loop mechanism with an adaptive Double-Dueling Deep Q-Network (D3QN) policy. Contribution/Results: Experiments demonstrate that, under identical resource constraints, the proposed approach significantly outperforms orthogonal multiple access (OMA) in multi-loop remote control task completion rate and robustness to dynamic environmental variations.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Quantify decision-making effectiveness in Goal-oriented systems.
• Optimize transmission and control in NOMA networks.
• Demonstrate NOMA's utility over OMA in remote control.

Innovation

• Introduces Goal-oriented Tensor (GoT) metric
• Uses POMDP for joint transmission-control optimization
• Employs Double-Dueling Deep Q-Network (D3QN)