Channel Prediction for mmWave Ground-to-Air Propagation under Blockage.

Area of Science:

• Wireless communication
• Signal processing
• Aerospace engineering

Background:

• Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) is crucial for future 5G and beyond networks.
• Millimeter wave (mmWave) frequencies used in GA links are prone to frequent signal blockages.
• Reliable channel information is lost during these link blockages, hindering communication continuity.

Purpose of the Study:

• To develop a novel channel prediction method for mmWave GA links during signal blockages.
• To ensure reliable channel information acquisition despite UAV trajectory-induced obstructions.
• To validate the prediction method's accuracy at 28 GHz.

Main Methods:

• Multipath components (MPCs) are grouped into distinct path bins using Euclidean distance of channel parameters.
• An autoregressive model is employed to forecast channel parameters within each path bin during blockages.
• Ray tracing simulations are utilized to generate channel data and validate the prediction model.

Main Results:

• The proposed method successfully predicts mmWave channel parameters during GA link blockages.
• Simulated results show a strong agreement between predicted and actual channel parameters.
• The path binning approach effectively isolates and forecasts distinct signal propagation paths.

Conclusions:

• The novel channel prediction method offers a viable solution for maintaining reliable mmWave GA communication during blockages.
• Accurate channel state information can be maintained for UAVs using this predictive approach.
• This work contributes to the robustness of future wireless networks supporting UAVs.