Enhancing the Accuracy of CSI-Based Positioning in Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) communication systems are being investigated intensively for positioning services. Enhancing the accuracy on these services in terms of accurate positioning of users is an important goal to improve related applications in the future. Convolutional Neural Networks (CNNs) has been proposed to infer the position of a user from Channel State Information (CSI) of a massive MIMO system. This paper investigates different architectures of CNNs to enhance the accuracy of a fingerprint-based positionina system. Three new CNNs has been proposed in which the Convolutional Layer (CL) and the Fully Connected (FC) layer are re-dimensioned. Batch Normalization (BN) layer is introduced to the layer structure of the newly proposed CNNs. The CNNs were trained, and accordingly mean error is measured. The first re-constructed CNN composed of 13 CLs, 7 BNs, and 3 FC layers has achieved the best accuracy out of the three models. It managed to achieve a mean error of 10.09 mm, that outperforms a similar work by 82 % in terms of positioning accuracy. Pruning was added to the layer structure of the newly proposed CNN s. It reduced the model size significantly, approximately by 65 % compared to a similar model of previous work.