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Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems

Pouya Agheli, Nikolaos Pappas, Marios Kountouris

TL;DR

The paper addresses resource-efficient goal-oriented connectivity in networked intelligent systems by introducing a self-decision goal-oriented multiple access scheme. It defines a GoE metric that jointly accounts for discrepancy error, resource consumption, and update utility, and derives optimal activation probabilities and threshold-based decision rules for three update-acquisition schemes. Using a KKT-based optimization and a practical algorithm, it jointly optimizes activation and weighting, with analysis for large attribute-state spaces. Simulations show the approach achieves at least 92% of the optimal GoE and can reduce channel load under semantics-aware operation, demonstrating substantial gains in efficiency for semantic communications over shared wireless channels.

Abstract

We design a self-decision goal-oriented multiple access scheme, where sensing agents observe a common event and individually decide to communicate the event's attributes as updates to the monitoring agents, to satisfy a certain goal. Decisions are based on the usefulness of updates, generated under uniform, change- and semantics-aware acquisition, as well as statistics and updates of other agents. We obtain optimal activation probabilities and threshold criteria for decision-making under all schemes, maximizing a grade of effectiveness metric. Alongside studying the effect of different parameters on effectiveness, our simulation results show that the self-decision scheme may attain at least 92% of optimal performance.

Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems

TL;DR

The paper addresses resource-efficient goal-oriented connectivity in networked intelligent systems by introducing a self-decision goal-oriented multiple access scheme. It defines a GoE metric that jointly accounts for discrepancy error, resource consumption, and update utility, and derives optimal activation probabilities and threshold-based decision rules for three update-acquisition schemes. Using a KKT-based optimization and a practical algorithm, it jointly optimizes activation and weighting, with analysis for large attribute-state spaces. Simulations show the approach achieves at least 92% of the optimal GoE and can reduce channel load under semantics-aware operation, demonstrating substantial gains in efficiency for semantic communications over shared wireless channels.

Abstract

We design a self-decision goal-oriented multiple access scheme, where sensing agents observe a common event and individually decide to communicate the event's attributes as updates to the monitoring agents, to satisfy a certain goal. Decisions are based on the usefulness of updates, generated under uniform, change- and semantics-aware acquisition, as well as statistics and updates of other agents. We obtain optimal activation probabilities and threshold criteria for decision-making under all schemes, maximizing a grade of effectiveness metric. Alongside studying the effect of different parameters on effectiveness, our simulation results show that the self-decision scheme may attain at least 92% of optimal performance.
Paper Structure (15 sections, 1 theorem, 25 equations, 3 figures, 1 algorithm)

This paper contains 15 sections, 1 theorem, 25 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Goal-Oriented Update Provision
  4. Transmission Success Probability
  5. Performance Metrics
  6. Effective Discrepancy Error (EDE)
  7. Effective Resource Consumption (ERC)
  8. Effective Utility of Updates (EUU)
  9. Multiple Access Design
  10. Problem Formulation
  11. Problem Solution
  12. Analysis for Large $I_n$
  13. Decision Making Criterion
  14. Simulation Results
  15. Conclusion

Key Result

Proposition 1

The expected discrepancy error probability $P_{{\rm e}, n}$ for the $n$-th attribute is derived as where $\mathcal{E}_n=\frac{1}{2^{|\mathcal{K}_n|}} \sum_{\ell=1}^{2^{|\mathcal{K}_n|}} \mathcal{E}_{n,\ell}$ denotes the probability that the $n$-th attribute is not successfully delivered to $M_t$ NMAs, where where $q_{k,n}$ is the probability that the $k$-th ISA's observation is correct. Moreover

Figures (3)

  • Figure 1: Goal-oriented medium access in a networked intelligent system.
  • Figure 2: The objective of $\mathcal{P}_2$ and its constraint versus the meta value threshold.
  • Figure 3: The interplay between the objective of $\mathcal{P}_2$ and the number of states in the DTMC for different sizes of required attributes.

Theorems & Definitions (2)

  • Proposition 1
  • Remark 1