Measurement Report Data Research Articles

Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater.

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.

Rainfall estimation using measurement report data from time-division long term evolution networks

Accurate rainfall measurement is essential for flood forecasting, effective water resources management, agriculture, etc. Many traditional rainfall monitoring methods, such as rainfall gauges, weather radars, and satellites, have been developed. Recently, a rainfall retrieval algorithm based on the radio connection between one telephone tower and another has seen widespread use across countries as it does not require any deployment procedures or costs. However, because underground fibre-optic cable networks have been widely introduced and deployed between telephone towers, usage of rainfall retrieval algorithms has reduced. Measurement report (MR) data from time-division long term evolution (TD-LTE) networks for providing instructions to the user terminal has the potential to estimate rainfall owing to the impact of rainfall-based attenuation on MR data. We present an analysis of MR data collected during the local flood season over 2 months (May and August 2019) in Huizhou city, southern China. Comparisons between estimated and observed rainfall exhibit promising results. Rainfall estimation from MR data is a potential alternative/complementary rainfall estimation method, as well as having use in runoff simulations, with acceptable accuracy (>0.8); this is particularly relevant in the context of disappearing microwave links between telephone towers in cellular communication networks. The rainfall estimation method proposed herein can also be implemented in rarely gauged or ungauged areas for flood simulation.

Context-Aware Telco Outdoor Localization

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile de- vices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4% better than state-of-the-art.

Prediction of a New Kind of MR Data

In the field of TD-LTE network problem analysis, compared with traditional methods such as DT(Driver Test) and CQT(Call Quality Test), MR (Measurement Report) has the advantages of comprehensive information and high efficiency, begins to get more and more attention and application. In order to solve the problem of limited open time of measurement data acquisition system, a data prediction method based on LSTM (Long Short-Term Memory) model is proposed. Selecting part of the MR parameters as the experimental object, training LSTM Model with measurement data in a district of Beijing. Experimental results show that the proposed method can predict MR data accurately. Compared with the traditional prediction model ARIMA (Autoregressive Integrated Moving Average Model), this method has lower prediction error and more stable generalization ability.