  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.
