Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network
This work provides an incremental improvement in indoor positioning accuracy for wireless network terminals.
This paper investigates new convolutional neural network (CNN) structures to improve indoor positioning using MIMO-based channel state information (CSI). The proposed residual convolutional NN structure achieves 2cm to 10cm better position accuracy than existing NN structures while maintaining a lower number of weights.
Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of training-evaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2cm to 10cm better position accuracy than known NN structures used as a reference.