1 January 2020

Joint Localization and Channel Estimation for UAV-Assisted Millimeter Wave Communications

Alexandropoulos, G., Vlachos, E., Smida, B.
2020 54th Asilomar Conference on Signals, Systems, and Computers
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Abstract

In this paper, we consider millimeter Wave (mmWave) communications between an Unmanned Aerial Ve- hicle (UA V) and a base station. By assuming that both com- munication nodes are equipped with arrays of multiple antenna elements, we focus on the joint estimation of the UA V position and the Multiple Input Multiple Output (MIMO) channel coefficients. Capitalizing on the line-of-sight signal propagation conditions and the beamspace representation of the wireless channel, we first estimate the UA V position. Using this estimation, we then present a matrix completion formulation for the recovery of the remaining non-line-of-sight components of the MIMO channel matrix, which is efficiently solved via the alternating direction method of multipliers. Selected simulation results for a 28GHz channel model verify that the proposed scheme is beneficial both in terms of estimation accuracy and resources utilization.

Type 1
Publication 2020 54th Asilomar Conference on Signals, Systems, and Computers
DOI 10.1109/ieeeconf51394.2020.9443514
Date January 2020
Research 6G UAVs

Key Contributions

  • Exploiting the line-of-sight (LOS) propagation conditions and beamspace representation of the wireless channel to estimate the UAV position.
  • Utilizing the estimated UAV position to formulate a matrix completion-based approach for recovering the non-line-of-sight (NLOS) components of the MIMO channel.
  • Demonstrating superior performance compared to baseline methods, requiring fewer channel training resources.

Results & Insights

NMSE performance of the proposed LOS channel estimation technique versus baseline methods.
NMSE performance of the proposed LOS channel estimation technique versus baseline methods.
The proposed method achieves significantly lower estimation error for the line-of-sight channel component across various scenarios, highlighting its effectiveness in leveraging UAV position information.

NMSE performance of the proposed method as a function of the number of channel training transmits.
NMSE performance of the proposed method as a function of the number of channel training transmits.
The results demonstrate that the proposed approach maintains low estimation error even with reduced channel training resources, offering a more efficient solution for joint localization and channel estimation in UAV-assisted mmWave systems.

Research 6G UAVs