Complex-Valued Convolutional Networks for Precise Positioning in NLoS Environment with Distributed MIMO

Recent advancements in deep neural network (NN) models have made them promising candidates for positioning devices in environments lacking line-of-sight (LoS) links. We propose several novel components and design choices for complexvalued NNs (CVNNs) to process signals received from distributed multiple input multiple output (MIMO) wireless systems for precise positioning in non-LoS (NLoS) environments. We design effective preprocessing solution to handle the wide dynamic range of radio frequency (RF) data; novel MIMO RF data tokenization approach with 1D convolutional layers to enable joint processing of all antennas; and novel components for CVNN designs. We further propose three highly effective <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R F}$</tex> data augmentation approaches to enhance model performance and reduce needs of large training datasets. Extensive comparison/ablation study is provided to support the proposals. With the proposed designs, we demonstrate the achievability of 2D positioning error less than 0.21 m more than 90 % of the time in a large NLoS environment.