MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI

Abstract

This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datasets to perform well, our method is specifically designed to operate with small-scale training datasets. This makes our approach more practical for real-world scenarios, where collecting a large amount of data can be challenging. Our proposed FC-AE-GPR framework combines two components: a Fully-Connected Auto-Encoder (FC-AE) and a Gaussian Process Regression (GPR) model. Our results show that the GPR model outperforms the CNN model when presented with small training datasets. However, the training complexity of GPR models can become an issue when the input sample size is large. To address this, we propose using the FC-AE to reduce the sample size by encoding the CSI before training the GPR model. Although the FC-AE model may require a larger training dataset initially, we demonstrate that the FC-AE is scenario independent. This means that it can be utilized in new and unseen scenarios without prior retraining. Therefore, adapting the FC-AE-GPR model to a new scenario requires only retraining the GPR model with a small training dataset.

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

“…Map-Assisted CSI/ADP MAP-CSI [88] AoA and ToA MAP-AT [91,92] CS AoA/AoD DC-SOMP [59][60][61] Fingerprinting DL CSI/ADP MLP [71,72,93,94], CNN [51,[73][74][75]95], RNN [53] AoA MLP [71] AoA and ToA MLP [71] RSSI MLP [71] Fingerprinting GPR CSI/ADP GPR [83], FC-AE-GPR [84], DCGPR [ need to be addressed. This section aims to introduce some of the main challenges in MIMO localization.…”

“…The fingerprint in distributed MIMO systems is usually the RSSI vector as proposed in [69,70,82]. On the other hand, the input in singlesite MIMO systems can be AoA/AoD vector, CSI or ADP data as proposed in [83,84].…”

“…The main advantage of GPR models over DL CNN models is that they can be trained on substantially smaller datasets. GPR models have shown the ability to train models with small-scale datasets due to the small number of hyper-parameters that define the model [84]. However, the GPR model does have its drawbacks.…”

“…The weights can also be defined by some similarity criteria calculated between the input and the reference fingerprint. Various similarity criteria have been established in wireless MIMO communication, such as normalized correlation [53,86,88], Joint Angle Delay Similarity Coefficient (JADSC) [63], Angular Similarity Coefficient Weight (ASCW) [89], and Weighted Mean Square Error (WMSE) [90].…”