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Positioning Error Compensation by Channel Knowledge Map in UAV Communication Missions

Chiya Zhang, Ting Wang, Chunlong He

TL;DR

This work tackles UAV trajectory optimization under positioning errors in UAV-IoT scenarios by introducing a Channel Knowledge Map (CKM) that predicts channel gains despite location noise. A CKM-based Positioning Error Compensation with PPO (PEC-PPO) framework reformulates the problem as an MDP and applies PPO2, integrating CKM for rate calculations and enabling online updating and confidence-guided power control. Simulation results show CKM robustness to positioning errors and environmental changes, with PEC-PPO achieving shorter flight times and stable communication compared to baselines. The approach provides a practical, adaptive solution for reliable and efficient UAV communications in GNSS-perturbed settings.

Abstract

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design.

Positioning Error Compensation by Channel Knowledge Map in UAV Communication Missions

TL;DR

This work tackles UAV trajectory optimization under positioning errors in UAV-IoT scenarios by introducing a Channel Knowledge Map (CKM) that predicts channel gains despite location noise. A CKM-based Positioning Error Compensation with PPO (PEC-PPO) framework reformulates the problem as an MDP and applies PPO2, integrating CKM for rate calculations and enabling online updating and confidence-guided power control. Simulation results show CKM robustness to positioning errors and environmental changes, with PEC-PPO achieving shorter flight times and stable communication compared to baselines. The approach provides a practical, adaptive solution for reliable and efficient UAV communications in GNSS-perturbed settings.

Abstract

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design.
Paper Structure (14 sections, 18 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 18 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. CKM-based PEC-PPO Scheme
  4. Construction of CKM
  5. Formulated Problem
  6. MDP Formulation
  7. State Space
  8. Action Space
  9. Reward Function
  10. PPO2-based UAV Trajectory Design with CKM
  11. Simulation Results
  12. Simulation Settings
  13. Results and Analysis
  14. Conclusion

Figures (9)

  • Figure 1: UAV-assisted Communication Scenario. Length is 1000m, width is 1000m, height is 750m, UAV flies between 250-750m, 15 GUs are distributed randomly between 0-250m, and buildings and plants are randomly set under 250m
  • Figure 2: Process of CKM construction. input: environment features, position pairs of GUs and the UAV in the simulation environment, noise labels, and output: channel gains
  • Figure 3: Detailed Construction and Utilization of CKM
  • Figure 4: The convergence of completion time(green) and rewards(red) under max time $160s$ and reward scaling factors $r_1=0.000001,r_2=0.0012,r_3=0.005$
  • Figure 5: trajectory of CKM-PPO under the positioning error of CEP=0, 1, 5, 10m
  • ...and 4 more figures