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Average AoI in Pinching Antenna-assisted WPCNs with Probabilistic LoS Blockage

Huimin Hu, Ruihong Jiang, Yanqing Xu, Jiarui Ma, Fang Fang

TL;DR

The paper addresses AoI minimization in a pinching-antenna (PA) assisted wireless powered communication network (WPCN) under probabilistic LoS blockage, proposing a probabilistic LoS model with $P_{ ext{LoS}}(d)=e^{-eta d^2}$ and deriving a closed-form average AoI $ar{A}= rac{Delta_T}{2}igl( rac{E(S^2)}{E(S)}+1igr)$ based on capacitor charging times and transmission success probabilities. It introduces a 1D optimization over the PA position $x_p^*= ext{argmin}_{x_p o[0,L]}ar{A}$, solvable via a simple search, and demonstrates that placing the PA closer to the IoT device improves both energy harvesting and LoS reliability. Key contributions include the probabilistic LoS blockage model, a tractable closed-form AoI expression, and an optimization framework that yields significant AoI reductions compared to fixed-antenna WPCNs. The work is practically relevant for real-time IoT applications requiring fresh status updates, enabling adaptive PA positioning to balance energy harvesting and reliable communication under environmental obstructions.

Abstract

This paper analyzes the age of information (AoI) for a pinching antenna (PA)-assisted wireless powered communication network (WPCN) with probabilistic line-of-sight (LoS) blockage. AoI is a key metric for evaluating the freshness of status updates in IoT networks, and its optimization is crucial for ensuring the performance of time-critical applications. To facilitate analysis and gain useful insights, we consider a representative scenario, where an IoT device harvests energy from a base station (BS) equipped with a PA and transmits data packets to it. The IoT device harvests energy via the PA until its capacitor is fully charged, then transmits status updates using all stored energy. We derive closed-form expressions for the average AoI by analyzing the capacitor charging time, transmission success probability, and inter-arrival time of successful updates. To minimize the average AoI, we formulate an optimization problem of PA position, and propose a one-dimensional search to solve it. The simulation results show that the optimal PA position is the one closest to the IoT device, and this conclusion can be extended to the multi-IoT devices frequency division multiple access (FDMA) scenario. The PA-based systems significantly outperform the conventional fixed-antenna systems.

Average AoI in Pinching Antenna-assisted WPCNs with Probabilistic LoS Blockage

TL;DR

The paper addresses AoI minimization in a pinching-antenna (PA) assisted wireless powered communication network (WPCN) under probabilistic LoS blockage, proposing a probabilistic LoS model with and deriving a closed-form average AoI based on capacitor charging times and transmission success probabilities. It introduces a 1D optimization over the PA position , solvable via a simple search, and demonstrates that placing the PA closer to the IoT device improves both energy harvesting and LoS reliability. Key contributions include the probabilistic LoS blockage model, a tractable closed-form AoI expression, and an optimization framework that yields significant AoI reductions compared to fixed-antenna WPCNs. The work is practically relevant for real-time IoT applications requiring fresh status updates, enabling adaptive PA positioning to balance energy harvesting and reliable communication under environmental obstructions.

Abstract

This paper analyzes the age of information (AoI) for a pinching antenna (PA)-assisted wireless powered communication network (WPCN) with probabilistic line-of-sight (LoS) blockage. AoI is a key metric for evaluating the freshness of status updates in IoT networks, and its optimization is crucial for ensuring the performance of time-critical applications. To facilitate analysis and gain useful insights, we consider a representative scenario, where an IoT device harvests energy from a base station (BS) equipped with a PA and transmits data packets to it. The IoT device harvests energy via the PA until its capacitor is fully charged, then transmits status updates using all stored energy. We derive closed-form expressions for the average AoI by analyzing the capacitor charging time, transmission success probability, and inter-arrival time of successful updates. To minimize the average AoI, we formulate an optimization problem of PA position, and propose a one-dimensional search to solve it. The simulation results show that the optimal PA position is the one closest to the IoT device, and this conclusion can be extended to the multi-IoT devices frequency division multiple access (FDMA) scenario. The PA-based systems significantly outperform the conventional fixed-antenna systems.

Paper Structure

This paper contains 10 sections, 3 theorems, 25 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. System Model
  4. Channel Model with Probabilistic LoS Blockage
  5. EH Model and Communication Model
  6. AoI model
  7. Analysis of the Average AoI
  8. Numerical results
  9. Conclusion
  10. Proof of Proposition \ref{['pro1']}

Key Result

Theorem 1

The probability of successful transmission of a data packet in time slot $t$ is given by where $\theta = \frac{D}{ B \Delta_T}$ and $\mathbb{I}(\cdot)$ denotes the indicator function, which equals 1 when the event in parentheses occurs and 0 otherwise.

Figures (5)

  • Figure 1: The system model.
  • Figure 2: An example of AoI dynamic evolution. $S_m$ denotes the interval time between two consecutive received data packet, $T_m$ is the time between two consecutive capacitor’s recharges, $Q_m$ is the area under $\Delta(t)$ corresponding to the $m$-th received data packet.
  • Figure 3: The probability of successful transmission of a data packet ($p_s{\rm}(x_p)$) versus the PA position $x_p$.
  • Figure 4: The average AoI versus the blockage parameter $\beta$ under different PA position $x_p$.
  • Figure 5: The average AoI versus the PA position $x_p$ and the capacitor of the IoT device $B_{\rm max}$.

Theorems & Definitions (5)

  • Theorem 1
  • proof
  • Proposition 1
  • proof
  • Theorem 2