Sensing information-assisted superimposed pilot-based channel estimation for UAV communication systems

Area of Science:

• Wireless Communication
• Signal Processing
• Robotics

Background:

• Unmanned aerial vehicles (UAVs) face connectivity challenges in industrial applications due to time-varying channels.
• Superimposed pilot schemes improve spectral efficiency but cause pilot-data interference, degrading performance.
• Accurate channel estimation is vital for reliable UAV operations.

Purpose of the Study:

• To propose a sensing information-assisted channel estimation method for UAV orthogonal frequency division multiplexing (OFDM) systems.
• To mitigate pilot-data interference and enhance channel estimation accuracy.
• To improve spectral efficiency and symbol detection reliability for mission-critical UAV communications.

Main Methods:

• Exploiting onboard UAV kinematic parameters (position, velocity) to establish prior bounds in delay-Doppler domains.
• Implementing a two-stage delay-Doppler denoising scheme with adaptive thresholding to reduce interference.
• Employing a sensing-assisted iterative decision-directed mechanism for refined channel estimation.

Main Results:

• The proposed method effectively eliminates error floors seen in conventional superimposed pilot schemes.
• Achieves rapid convergence and optimal symbol detection performance, even at low pilot power ratios.
• Demonstrates significant improvements in spectral efficiency for dynamic UAV communication scenarios.

Conclusions:

• The sensing information-assisted method provides robust and accurate channel estimation for UAVs.
• It enhances the reliability and efficiency of wireless networks supporting industrial UAV applications.
• This approach is crucial for enabling advanced UAV operations in challenging environments.