This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.

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Abstract-One of the basic aspects of Massive MIMO (MaMi) that is in the focus of current investigations is its potential of using low-cost and energy-efficient hardware. It is often claimed that MaMi will allow for using analog-to-digital converters (ADCs) with very low resolutions and that this will result in overall improvement of energy efficiency. In this contribution, we perform a parametric energy efficiency analysis of MaMi uplink for the entire base station receiver system with varying ADC resolutions. The analysis shows that, for a wide variety of system parameters, ADCs with intermediate bit resolutions (4 -10 bits) are optimal in energy efficiency sense, and that using very low bit resolutions results in degradation of energy efficiency.