Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks

Ye, Xiaokang; Yin, Xuefeng; Cai, Xuesong; Yuste, Antonio Pérez; Xu, Hongliang

doi:10.1109/access.2017.2712131

Cited by 68 publications

(47 citation statements)

References 68 publications

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“…By learning and training the big datasets in wireless communications, the network can be more intelligent to achieve better performance and adapt to various applications. A popular application of machine learning in wireless communications is indoor and outdoor localization/positioning [23]- [27]. In [23], features that represent propagation conditions were extracted from ultra-wideband (UWB) measurement data, then classification and regression algorithms were developed based on SVM.…”

Section: Machine Learning In Wireless Communicationsmentioning

confidence: 99%

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Huang

Wang

Bai

et al. 2020

IEEE Trans. Big Data

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The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

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Section: Machine Learning In Wireless Communicationsmentioning

confidence: 99%

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Huang

Wang

Bai

et al. 2020

IEEE Trans. Big Data

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“…Mondal et al evaluated the positioning accuracy of a radio fingerprinting algorithm in commercially deployed LTE networks operating on 800 MHz, 1800 MHz, and 2600 MHz frequency bands [33]. Ye et al utilized unique mapping between the characteristics of a radio channel formulated as a fingerprint vector and a geographic location [34]. The "shape" of the channel frequency response (CFR) could be used to construct a CSI-based fingerprint database.…”

Section: Related Workmentioning

confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.technologies require additional devices, making these systems impractical. Additionally, in signal NLOS propagation, the performance of the positioning systems will be greatly reduced [5].In contrast, wireless fingerprint positioning technology has received much attention owing to its simplicity and practicality with existing infrastructure and hardware. Many complex LTE signal cues are hidden in the surrounding environment, and the goal of wireless fingerprint-based localization is to discover these cues and use them effectively to determine UE positions [6]. Compared to satellite navigation positioning systems and other wireless positioning technologies, fingerprint-based positioning techniques have many merits. First, low-energy consumption sensors in UE require much less energy. Second, most of the fingerprint-based positioning technologies require no additional devices or infrastructure. Finally, fingerprint-based positioning can be achieved by using ubiquitous smartphones and signals from LTE base stations (BSs) [6,7]. Fingerprint positioning technology works in two phases: an offline training phase and an online matching phase [8]. During the training phase, signal measurements can be used, which are usually received signal strength indicator (RSSI), reference signal receivin...

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“…In [24], a feature extraction algorithm is applied to select channel parameters with non-redundant information that are calculated from the LTE downlink signals. A feedforward neural network with the input of fingerprint vectors and the output of UEs' known locations is trained and used by UEs to estimate their positions.…”

Section: Related Workmentioning

confidence: 99%

“…x t,true − x t,est 2 + y t,true − y t,est 2 (24) where N TF represents the overall number of test fingerprints extracted from all the test points. Cumulative Distribution Function (CDF) of the localization error is used to provide a better characterization of the localization performance.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

CSI-based fingerprinting for indoor localization using LTE Signals

Pecoraro

Domenico

Cianca

et al. 2018

EURASIP J. Adv. Signal Process.

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This paper addresses the use of channel state information (CSI) for Long Term Evolution (LTE) signal fingerprinting localization. In particular, the paper proposes a novel CSI-based signal fingerprinting approach, where fingerprints are descriptors of the "shape" of the channel frequency response (CFR) calculated on CSI vectors, rather than direct CSI vectors. Experiments have been carried out to prove the feasibility and the effectiveness of the proposed method and to study the impact on the localization performance of (i) the bandwidth of the available LTE signal and (ii) the availability of more LTE signals transmitted by different eNodeB (cell diversity). Comparisons with other signal fingerprinting approaches, such as the ones based on received signal strength indicator or reference signal received power, clearly show that using LTE CSI, and in particular, descriptors as fingerprints, can bring relevant performance improvement.

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Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks

Cited by 68 publications

References 68 publications

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

CSI-based fingerprinting for indoor localization using LTE Signals

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