This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.
This figure shows that the proposed velocity-aided method significantly outperforms conventional training-based approaches in terms of channel estimation accuracy (lower NMSE), especially at higher UAV velocities and moderate to high SNR levels, validating the effectiveness of incorporating velocity information.
The results indicate that the proposed method achieves lower BER for higher data rates and faster UAV speeds compared to baseline methods, demonstrating its practical benefit for reliable mmWave massive MIMO communication with moving platforms.