| Authors: | |
| Xiaoyu Sun | |
| Chi Wu | |
| Xiqi Gao | |
| Geoffrey Ye Li |
One Sentence Summary:
investigate fingerprint-based localization
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 frequencydivision 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. Index Terms-Localization, fingerprint, deep convolutional neural networks, DCNN, massive MIMO-OFDM This work was supported by the National Key R&D Program of China under Grant 2018YFB1801103, the National Natural Science Foundation of China under Grants 61631018, 61801114, 61761136016, and 61771264, the Natural Science Foundation of Jiangsu Province under Grant BK20170688, the Fundamental Research Funds for the Central Universities, and the Huawei Cooperation Project.