MLP-mmWP: High-Precision Millimeter Wave Positioning Based on MLP-Mixer Neural Networks

Area of Science:

• Wireless communication
• Artificial intelligence
• Deep learning

Background:

• Millimeter wave (MMW) communication offers wide bandwidth and high-speed transmission, crucial for the Internet of Everything (IoE).
• Accurate data transmission and localization are key challenges in MMW applications like autonomous vehicles and intelligent robots.
• Artificial intelligence (AI) is increasingly applied to address MMW communication challenges.

Purpose of the Study:

• To propose MLP-mmWP, a novel deep learning method for user localization using MMW communication information.
• To evaluate the performance of MLP-mmWP in estimating localization based on beamformed fingerprints (BFFs).
• To demonstrate the method's effectiveness in both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions.

Main Methods:

• Development of MLP-mmWP, a deep learning model utilizing the MLP-Mixer neural network architecture.
• Employing seven sequences of beamformed fingerprints (BFFs) for localization estimation.
• Validation using a public dataset and comparison against state-of-the-art methods.

Main Results:

• MLP-mmWP achieves a positioning mean absolute error of 1.78 m in a 400 × 400 m² area.
• The 95th percentile prediction error is 3.96 m, showing significant improvements.
• The method outperforms existing state-of-the-art techniques in MMW positioning accuracy.

Conclusions:

• MLP-mmWP is the first method to apply the MLP-Mixer neural network for MMW positioning.
• The proposed deep learning approach enhances localization accuracy in MMW communication systems.
• Experimental results confirm the superiority of MLP-mmWP over current methods.