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Fairness-aware Age-of-Information Minimization in WPT-Assisted Short-Packet Data Collection for mURLLC

Yao Zhu, Xiaopeng Yuan, Yulin Hu, Bo Ai, Ruikang Wang, Bin Han, Anke Schmeink

TL;DR

This paper examines Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services, and focuses on information freshness, using Age-of-Information (AoI) as the key performance metric.

Abstract

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services.} We focus on information freshness, using Age-of-Information (AoI) as the key performance metric. Specifically, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings demonstrate the convexity of the problem, enabling efficient solutions. We introduce the concept of AoI-oriented cluster capacity and analyze the relationship between the number of supported devices and network AoI performance. Numerical simulations validate our proposed approach's effectiveness in enhancing AoI performance, highlighting its potential for guiding the design of future IoT systems requiring mURLLC services.

Fairness-aware Age-of-Information Minimization in WPT-Assisted Short-Packet Data Collection for mURLLC

TL;DR

This paper examines Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services, and focuses on information freshness, using Age-of-Information (AoI) as the key performance metric.

Abstract

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services.} We focus on information freshness, using Age-of-Information (AoI) as the key performance metric. Specifically, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings demonstrate the convexity of the problem, enabling efficient solutions. We introduce the concept of AoI-oriented cluster capacity and analyze the relationship between the number of supported devices and network AoI performance. Numerical simulations validate our proposed approach's effectiveness in enhancing AoI performance, highlighting its potential for guiding the design of future IoT systems requiring mURLLC services.
Paper Structure (21 sections, 5 theorems, 40 equations, 9 figures, 1 algorithm)

This paper contains 21 sections, 5 theorems, 40 equations, 9 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model and Problem Statement
  4. System Description
  5. WPT and WIT Channel Model
  6. Characterization of the Time-Average AoI
  7. Packet Error Probability in Finite Blocklength Regime
  8. Problem Statement
  9. Problem Reformulation
  10. Problem Reformulation
  11. Optimal Solutions of Problem \ref{['problem:reformulation']}
  12. Cluster Capacity and Efficient Solutions
  13. Cluster Capacity
  14. Low-complexity Algorithm for solving Problem \ref{['problem:reformulation']}
  15. Numerical Simulations
  16. ...and 6 more sections

Key Result

Lemma 1

With a fixed update strategy $M_i=m_{c,i}+m_{r,i}$, the start time of the update $a_{i,k}(t)$ does not influence the time-average AoI $\bar{\Delta}_i$ over the time infinite horizon, i.e., $t\to\infty$.

Figures (9)

  • Figure 1: Evolution of AoI with the Event $X_{i,\tilde{k}}$.
  • Figure 2: Equivalent update scheduling policies.
  • Figure 3: The impact of an additional update to the system in both saturated and unsaturated case with an additional update duration $m^{re}_{r,j}$.
  • Figure 4: The impact of charging duration $m_{c,1}$ and update duration $m_{r,1}$ on the error probability $\varepsilon_1$, where the device is located at range $d_i=1$ m.
  • Figure 5: The impact of charging duration $m_{c,1}$ and update duration $m_{r,1}$ on the average AoI $\bar{\Delta}_1$ where the device is located at range $d_i=1$ m.
  • ...and 4 more figures

Theorems & Definitions (10)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Corollary 1
  • proof
  • Corollary 2
  • proof
  • Lemma 3
  • proof