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UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication

Wenhao Zhuang, Xinyu He, Yuyi Mao, Juan Liu

TL;DR

A convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power, and demonstrate the critical tradeoff between sensing and learning performance.

Abstract

Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific data utilities in different AI applications. In this letter, a full-duplex unmanned aerial vehicle (UAV)-enabled wireless network providing sensing and edge learning services is investigated. To maximize the learning performance while ensuring sensing quality, a convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power. Simulation results show that the proposed algorithm significantly outperforms the baselines and demonstrate the critical tradeoff between sensing and learning performance.

UAV-Enabled Wireless Networks for Integrated Sensing and Learning-Oriented Communication

TL;DR

A convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power, and demonstrate the critical tradeoff between sensing and learning performance.

Abstract

Future wireless networks are envisioned to support both sensing and artificial intelligence (AI) services. However, conventional integrated sensing and communication (ISAC) networks may not be suitable due to the ignorance of diverse task-specific data utilities in different AI applications. In this letter, a full-duplex unmanned aerial vehicle (UAV)-enabled wireless network providing sensing and edge learning services is investigated. To maximize the learning performance while ensuring sensing quality, a convergence-guaranteed iterative algorithm is developed to jointly determine the uplink time allocation, as well as UAV trajectory and transmit power. Simulation results show that the proposed algorithm significantly outperforms the baselines and demonstrate the critical tradeoff between sensing and learning performance.
Paper Structure (13 sections, 2 theorems, 18 equations, 3 figures)

This paper contains 13 sections, 2 theorems, 18 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Description
  4. Channel and Signal Models
  5. Learning-Oriented Communication and Target Sensing
  6. Problem Formulation
  7. Proposed Solution
  8. Uplink Time Allocation
  9. UAV Trajectory Optimization
  10. UAV Transmit Power Optimization
  11. Convergence and Complexity Analysis
  12. Simulation Results
  13. Conclusion

Key Result

Lemma 1

Given $\bm{\alpha} \succeq \bm{0}$, $f_n(\bm{x})$, $g_n(\bm{x})>0, \forall \bm{x}$ and $n\in\mathcal{N}$, where the equality is achieved at $\alpha_n= {\sqrt{f_n(\bm{x})}}\slash {g_n(\bm{x})}, \forall n$.

Figures (3)

  • Figure 1: . A full-duplex UAV-enabled wireless network for integrated sensing and learning-oriented communication.
  • Figure 2: . Block diagram of the proposed algorithm for $\textbf{P}_1$.
  • Figure 3: . Learning performance and UAV trajectory of different algorithms. The progress bars in (c) and (d) represent the proportions of data samples being collected at each device.

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Lemma 2
  • proof