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AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks

Yusi Long, Songhan Zhao, Shimin Gong, Bo Gu, Dusit Niyato, Xuemin, Shen

TL;DR

This work addresses the challenge of keeping sensing data fresh in a multi-UAV backscatter network by minimizing the long-term AoI. It introduces AoI-STO, a Lyapunov-based framework that decomposes the stochastic optimization into per-slot subproblems solved via BCD and SCA, jointly optimizing GU access, UAV beamforming, and UAV trajectories. The approach leverages backscatter sensing and NOMA forwarding to boost capacity and information freshness, achieving over 50% AoI reduction and improved fairness compared to baselines. The results demonstrate efficient per-slot optimization, robust trajectory planning, and significant gains in data timeliness for large-scale UAV-enabled sensing deployments. The work has practical implications for real-time sensing and disaster-response networks where data freshness is critical.

Abstract

This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sensing data from the GUs and then forwards the sensing data to the remote BS. The GUs first backscatter their data to the UAVs and then all UAVs forward data to the BS by the nonorthogonal multiple access (NOMA) transmissions. We formulate a multi-stage stochastic optimization problem to minimize the long-term time-averaged age-of-information (AoI) by jointly optimizing the GUs' access control, the UAVs' beamforming, and trajectory planning strategies. To solve this problem, we first model the dynamics of the GUs' AoI statuses by virtual queueing systems, and then propose the AoI-aware sensing scheduling and trajectory optimization (AoI-STO) algorithm. This allows us to transform the multi-stage AoI minimization problem into a series of per-slot control problems by using the Lyapunov optimization framework. In each time slot, the GUs' access control, the UAVs' beamforming, and mobility control strategies are updated by using the block coordinate descent (BCD) method according to the instant GUs' AoI statuses. Simulation results reveal that the proposed AoI-STO algorithm can reduce the overall AoI by more than 50%. The GUs' scheduling fairness is also improved greatly by adapting the GUs' access control compared with typical baseline schemes.

AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks

TL;DR

This work addresses the challenge of keeping sensing data fresh in a multi-UAV backscatter network by minimizing the long-term AoI. It introduces AoI-STO, a Lyapunov-based framework that decomposes the stochastic optimization into per-slot subproblems solved via BCD and SCA, jointly optimizing GU access, UAV beamforming, and UAV trajectories. The approach leverages backscatter sensing and NOMA forwarding to boost capacity and information freshness, achieving over 50% AoI reduction and improved fairness compared to baselines. The results demonstrate efficient per-slot optimization, robust trajectory planning, and significant gains in data timeliness for large-scale UAV-enabled sensing deployments. The work has practical implications for real-time sensing and disaster-response networks where data freshness is critical.

Abstract

This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sensing data from the GUs and then forwards the sensing data to the remote BS. The GUs first backscatter their data to the UAVs and then all UAVs forward data to the BS by the nonorthogonal multiple access (NOMA) transmissions. We formulate a multi-stage stochastic optimization problem to minimize the long-term time-averaged age-of-information (AoI) by jointly optimizing the GUs' access control, the UAVs' beamforming, and trajectory planning strategies. To solve this problem, we first model the dynamics of the GUs' AoI statuses by virtual queueing systems, and then propose the AoI-aware sensing scheduling and trajectory optimization (AoI-STO) algorithm. This allows us to transform the multi-stage AoI minimization problem into a series of per-slot control problems by using the Lyapunov optimization framework. In each time slot, the GUs' access control, the UAVs' beamforming, and mobility control strategies are updated by using the block coordinate descent (BCD) method according to the instant GUs' AoI statuses. Simulation results reveal that the proposed AoI-STO algorithm can reduce the overall AoI by more than 50%. The GUs' scheduling fairness is also improved greatly by adapting the GUs' access control compared with typical baseline schemes.
Paper Structure (23 sections, 3 theorems, 38 equations, 10 figures, 2 tables, 1 algorithm)

This paper contains 23 sections, 3 theorems, 38 equations, 10 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. UAV-assisted Wireless Sensing Networks
  4. Backscatter-aided AoI Minimization
  5. Multi-UAV NOMA Transmissions
  6. System model
  7. Fly-to-Sense-and-Forward (FSF) Protocol
  8. Backscatter-aided Data Sensing
  9. Multi-UAV NOMA Transmissions
  10. Lyapunov Optimization for AoI Minimization
  11. AoI Dynamics and Long-term AoI Minimization
  12. Per-slot Decomposition via Lyapunov Optimization
  13. Per-slot Access Control, Beamforming Optimization, and Mobility Control
  14. The UAVs' Beamforming Optimization
  15. The UAVs' Mobility Control and Time Allocation
  16. ...and 8 more sections

Key Result

Proposition 1

For each GU-$k$, $k \in \mathcal{K}$, a virtual queue $X_k(i)$ can be constructed with initial zero state, i.e., $X_k(0)=0$, and the queue dynamics given by: If $X_k(i)$ is mean rate stable, i.e., $\lim_{i\rightarrow\infty}\frac{1}{i}E[|X_k(i)|]=0$, the satisfaction of the inequality in con:age can be ensured.

Figures (10)

  • Figure 1: The UAVs' planning for sensing, flying, and forwarding phases in a multi-UAV-assisted wireless network.
  • Figure 2: The AoI dynamics of the GU-$k$.
  • Figure 3: The overall algorithm framework
  • Figure 4: Convergence in two time slots of the SCA algorithm
  • Figure 5: AoI dynamics with different access control schemes.
  • ...and 5 more figures

Theorems & Definitions (3)

  • Proposition 1
  • Proposition 2
  • Proposition 3