Abstract
Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint-based localization for massive multiple- input multiple-output (MIMO) orthogonal frequency- division multiplexing (OFDM) systems with deep convolutional neural networks (DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. Numerical simulation results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.
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