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Goal-Oriented Random Access (GORA)

Ahsen Topbas, Cagri Ari, Onur Kaya, Elif Uysal

TL;DR

Goal-Oriented Random Access (GORA) tackles joint data generation and channel access in a shared collision channel to minimize a goal-driven AoI penalty $h( abla)$. The authors derive a symmetric stationary policy with parameters $(b,\tau,\Gamma)$, analyze the resulting finite-state Markov chain and renewal processes, and establish optimality conditions (Theorem 1) linking buffer timing and waiting times to long-term performance. They show GORA can outperform conventional Threshold ALOHA and Slotted ALOHA under both monotone and non-monotone AoI penalties by sending aged samples that better serve the destination's task, with optimal buffering offset $b^*$ adapting to network size. The work highlights the value of integrating data generation with transmission goals and suggests extensions to heterogeneous goals and reservation-based access strategies.

Abstract

We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function of the Age of Information. Our findings reveal that, under certain conditions, it may be desirable for transmitters to delay channel access intentionally and, when accessing the channel, transmit aged samples to reach a specific goal at the receiver.

Goal-Oriented Random Access (GORA)

TL;DR

Goal-Oriented Random Access (GORA) tackles joint data generation and channel access in a shared collision channel to minimize a goal-driven AoI penalty . The authors derive a symmetric stationary policy with parameters , analyze the resulting finite-state Markov chain and renewal processes, and establish optimality conditions (Theorem 1) linking buffer timing and waiting times to long-term performance. They show GORA can outperform conventional Threshold ALOHA and Slotted ALOHA under both monotone and non-monotone AoI penalties by sending aged samples that better serve the destination's task, with optimal buffering offset adapting to network size. The work highlights the value of integrating data generation with transmission goals and suggests extensions to heterogeneous goals and reservation-based access strategies.

Abstract

We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function of the Age of Information. Our findings reveal that, under certain conditions, it may be desirable for transmitters to delay channel access intentionally and, when accessing the channel, transmit aged samples to reach a specific goal at the receiver.

Paper Structure

This paper contains 5 sections, 4 theorems, 25 equations, 6 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Steady State Analysis
  4. Optimal Policy and Numerical Results
  5. Conclusion

Key Result

Lemma 1

The FSMC $\mathbf{A}^{b,\Gamma}[\ell]$ has a unique steady-state distribution, which matches the steady-state distribution of the FSMC $\mathbf{A}^{\Gamma}[\ell]$ defined in atabay2020improving.

Figures (6)

  • Figure 1: System Model.
  • Figure 2: The optimal parameter $b^*$ and expected penalty at the end of a renewal interval, ${\mathbb E \left[ h((b^* + \Gamma^* + Y + 1)d) \right]}$, for $n=1000$ and a simple convex goal function.
  • Figure 3: The optimal parameter $b^*$ for a simple convex goal function as the network size $n$ varies from 500 to 2500.
  • Figure 4: The performance evaluation of GORA, TA yavascan2021analysisatabay2020improving and SA for the goal function depicted in Fig. \ref{['fig:h1_b']}.
  • Figure 5: The optimal parameter $b^*$ for a goal function with two minima as the network size $n$ varies from 500 to 2500.
  • ...and 1 more figures

Theorems & Definitions (6)

  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Corollary 1
  • Corollary 2