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Joint Transmission and Control in a Goal-oriented NOMA Network

Kunpeng Liu, Shaohua Wu, Aimin Li, Qinyu Zhang

TL;DR

The paper introduces the Goal-oriented Tensor (GoT) as a closed-loop utility metric for goal-oriented remote control over wireless links and formulates a joint transmission-control problem in a pull-based NOMA network as a partially observable Markov decision process (POMDP). A belief-state construction is derived to address partial observability, and a Double-Dueling Deep Q-Network (D3QN) is trained to adapt power allocation and control actions. The GoT for each source is GoT_{t,i}^{\pi}=C_1(X_{t,i},\phi_t)+\alpha C_2(P_{t,i})+\beta C_3(C_{t,i}), and the long-term objective minimizes the average GoT. Simulation results show a fundamental trade-off between transmission efficiency and control fidelity, with NOMA outperforming OMA in multi-loop remote control scenarios, highlighting GoT-based optimization as a promising framework for goal-oriented networks. GoT-based framework for goal-oriented wireless control, framed as a POMDP with belief-state augmentation and solved via D3QN, demonstrates superior utility and actionable trade-offs in multi-loop NOMA remote control systems, with $GoT_{t,i}^{\pi}$ guiding power and actuation decisions.

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.

Joint Transmission and Control in a Goal-oriented NOMA Network

TL;DR

The paper introduces the Goal-oriented Tensor (GoT) as a closed-loop utility metric for goal-oriented remote control over wireless links and formulates a joint transmission-control problem in a pull-based NOMA network as a partially observable Markov decision process (POMDP). A belief-state construction is derived to address partial observability, and a Double-Dueling Deep Q-Network (D3QN) is trained to adapt power allocation and control actions. The GoT for each source is GoT_{t,i}^{\pi}=C_1(X_{t,i},\phi_t)+\alpha C_2(P_{t,i})+\beta C_3(C_{t,i}), and the long-term objective minimizes the average GoT. Simulation results show a fundamental trade-off between transmission efficiency and control fidelity, with NOMA outperforming OMA in multi-loop remote control scenarios, highlighting GoT-based optimization as a promising framework for goal-oriented networks. GoT-based framework for goal-oriented wireless control, framed as a POMDP with belief-state augmentation and solved via D3QN, demonstrates superior utility and actionable trade-offs in multi-loop NOMA remote control systems, with guiding power and actuation decisions.

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.

Paper Structure

This paper contains 12 sections, 10 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Semantics and Context Dynamics
  4. NOMA Uplink and Error Rate
  5. Goal-oriented Transmission and Control
  6. Problem Formulation
  7. Metric to Characterize the Goal
  8. POMDP Formulation
  9. Belief State
  10. Deep Reinforcement Learning Method
  11. Simulation Results
  12. Conclusion

Figures (4)

  • Figure 1: Goal-oriented NOMA Network Model.
  • Figure 2: Learning curve of D3QN and comparison of baselines when SNR = 17dB, $\alpha=0.2$.
  • Figure 3: The GoT performance comparison of NOMA and OMA under different SNR and transmission cost coefficients $\alpha$.
  • Figure 4: The transmission power, $C_1+\beta C_3$, and average GoT under different transmission cost coefficient $\alpha$ with SNR = 17dB, where $C_1+\beta C_3$ evaluats the effectiveness of the system's control performance.