Title: Fast fingerprint localisation based on product quantisation and convolution neural network in a massive MIMO system

Authors: Yijie Ren; Xiaojun Wang; Lin Liu; Xiaoshu Chen

Addresses: National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 211100, China ' National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 211100, China; Purple Mountain Laboratories, Nanjing, 211111, China ' National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 211100, China ' National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, 211100, China

Abstract: Location-based service (LBS) has recently been popular, such as auto-driving, navigation, and tracking. Fingerprint localisation is one of the most effective localisation schemes for both indoor and outdoor localisation. In this paper, fingerprint localisation algorithms are researched based on a massive multiple-in-multiple-out (MIMO) system. Firstly, the extraction of angle-delay channel power matrix (ADCPM) fingerprints and the channel model are introduced. Then, two new fast fingerprint localisation algorithms based on product quantisation (PQ) and convolution neural network (CNN) are proposed, respectively. PQ and CNN are applied to process the data in the online matching phase. Compared with other previously known positioning techniques, the test results show that the proposed algorithms achieve high accuracy, reduce delay, and greatly reduce computational complexity.

Keywords: fingerprint localisation; massive multiple-in-multiple-out; MIMO; product quantisation; convolution neural network; CNN.

DOI: 10.1504/IJSNET.2022.125269

International Journal of Sensor Networks, 2022 Vol.40 No.1, pp.67 - 75

Received: 04 Dec 2021
Accepted: 05 Dec 2021
Published online: 05 Sep 2022 *

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