Name
Title | ||
|---|---|---|
Deep Convolutional Neural Networks Enabled Fingerprint Localization for Massive MIMO-OFDM System. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GLOBECOM38437.2019.9013802 | GLOBECOM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyu Sun | 1 | 95 | 16.54 |
| Chi Wu | 2 | 0 | 0.34 |
| Xiqi Gao | 3 | 3043 | 217.05 |
| Geoffrey Ye Li | 4 | 9071 | 660.27 |