Minimization Of Drive Tests Research Articles

An AI-Enabled Framework to Defend Ingenious MDT-Based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

On the Improvement of Cellular Coverage Maps by Filtering MDT Measurements

Cellular networks are constantly evolving, driven by changes in user behavior and device capabilities. To ensure that networks adapt to these changes, it is of vital importance for mobile operators to have a good understanding of how well their network meets subscriber needs. For this purpose, the Minimization of Drive Test (MDT) feature has been standardized, allowing operators the cost-effective provision of geolocated network performance statistics and radio events. However, in practice, positioning errors severely limit the potential of MDT measurements. In this paper, an in-depth analysis of a large MDT dataset taken from a commercial Long-Term Evolution (LTE) network shows for the first time several sources of positioning errors in MDT measurements not previously reported in the literature. To address these, a novel heuristic filtering algorithm is proposed to discard samples with inaccurate location data. Method assessment is done by checking the impact of filtering on the coverage map built with a real MDT dataset. Results show that the proposed filtering method significantly improves the accuracy of coverage maps by eliminating unreliable measurements.

Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework

The fifth generation (5G) of mobile networks connects people, things, data, applications, transport systems, and cities in smart networked communication environments. With the growth in the amount of generated data, the number of wirelessly connected machines, traffic types, and associated requirements, ensuring high-quality mobile connectivity becomes incredibly difficult for technology suppliers. Mobile operators and network vendors enrolling in 5G face far more rapid demands than any technology before, and at the same time need to ensure efficiency and reliability in the network operations. In fact, intelligent forecasting and decision-making strategies are several of the centerpieces of current artificial intelligence research in various domains. Due to its strong fitting ability, machine learning is seen to have great potential to be employed to solve telecommunication networks’ optimization problems that range from the design of hardware elements to network self-optimization. This paper addresses the question of how to apply artificial intelligence to 5G radio access control and feed ML techniques with radio characteristic-based automatic data collection to achieve ML-based evaluation of 5G performance. The proposed methodology endorses ML tools for the 5G portfolio scenarios requirements assessment and integrates into the mature methods for network performance optimization: self-organizing networks (SON) and minimization of drive tests (MDT). In this context, the proposed treatment guides future network deployments and implementations adopted on a 3GPP standard basis.

Machine Learning-Based Integrated Wireless Sensing and Positioning for Cellular Network

Integrated sensing and communication (ISAC) is one of the most promising technical directions in B5G/6G era. Nevertheless, it is challenging for network operators to provide proper human-centric services (HCSs), appropriately with the explosive growth of mobile terminals in constantly changing scenarios. Scenario sensing is the foundation to provide users with intelligent context-aware services, while the combination of machine learning (ML) and automatic data collection enables network operators to become more flexible in decision-making and planning. In this article, an ML-based integrated indoor/outdoor (IO) sensing and positioning framework is proposed to improve the potential of ML in the context of cellular network service management. First, large amounts of measurement reports (MRs) are collected over Layer 3 at user equipment (UE) and evolved NodeBs (eNBs) in urban areas, including both indoor and outdoor samples to simulate the minimization of drive test (MDT). Then, a random-forest-based IO classifier is implemented to sense the mobile scenarios and filter the positioning fingerprints. Subsequently, the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (WKNN)-based Enhanced Cell ID (ECID) merging with the MR method is used for cellular positioning. The performance evaluation has shown that the positioning error of the MRs after denoising is effectively reduced compared with the conventional fingerprint-based positioning. Specifically, under the 67% standard defined by Federal Communications Commission (FCC), the resulting positioning error is about 4% lower than the case without preprocessing. In addition, comparative experiments are conducted to discuss the impact of different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> values at different data sizes of fingerprints. Moreover, the further data analysis shows that MRs collected in the mild indoor (MI) scenarios have a positive effect on the overall positioning error, which indicates that refined contextualizations contribute to the cellular positioning for HCS upgrade.

Semi-supervised learning-based coverage hole detection in cellular networks

For any time-critical mobile network-dependent applications and services, coverage is one of the prominent factors for providing the best Quality of Service (QoS) and Quality of Experience (QoE). A simple Coverage Hole (CH) may degrade the performance and the reputation of any operator by reducing the Key Performance Indicators (KPIs). This is one of the important aspects which need to be planned from the phase of network deployment throughout the whole operational stage. Many factors can cause CH such as attenuation, obstacles and improper network planning. Traditionally, a Drive Test (DT) used to be carried out in order to assess the quality of the mobile network signal. With technological advancement, DT has been replaced by the Minimization of Drive Test (MDT) and included as a part of Self-Organizing Networkss (SONs). The MDT process is applicable to networks that operate in 3G, 4G and 5G technologies. With this method, operators are able to measure network performance with the help of end users' devices. Thus, the network can be managed more conveniently, performance is improved, quality is increased, and maintenance costs are reduced for the network. However, the processing of MDT at the operators' side remains time-consuming and complex especially for CH analysis and detection from mobile network data. Therefore, we present a method by utilising Semi-Supervised Learning (SSL) in this paper so that this task becomes uncomplicated with improved accuracy. Our results show that the proposed method achieves better accuracy than the usual classification algorithm.

MDT-based Intelligent Route Selection for 5G-Enabled Connected Ambulances.

The fifth generation of cellular network (5G) can facilitate in-ambulance patient monitoring, diagnosis, and treatment by a remote specialist. However, 5G coverage and link quality can vary in time and location. The ambulance route selection can help meet the communication requirements of the in-ambulance applications. In this paper, we propose an innovative ambulance route selection framework which combines the communication requirements along with the network coverage and resources. The framework leverages the minimization of drive test (MDT) data to estimate the network coverage along the ambulance routes. To address the uneven distribution of location-based user-generated MDT data, we examine the performance and trustworthiness of several interpolation techniques to enrich the global MDT map for route selection. A simulated analysis shows that the proposed framework can dynamically adapt to varying application requirements as well as rapidly changing network conditions such as outages. Results also reveal that nearest neighbor and kriging interpolation techniques help complement the proposed framework by addressing the data sparsity problem.

Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM’s cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent’s sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT-driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks.

Solar radio emission as a disturbance of radiomobile networks

This paper analyses the effects of solar radio emissions in the radiomobile context, for the first time leveraging massive European Telecommunications Standards Institute (ETSI) 3rd Generation Partnership Program (3GPP) Minimization of Drive Test (MDT) radio measures produced by 4G LTE (Long Term Evolution) terminals and by 4G LTE Base Station cells. A method to study solar noise effects starting from radiomobile 3GPP standard MDT measures is illustrated and correlated with the excellent 10.7 cm (2800 MHz) indicator of solar activity (National Research Council Canada). The effects of solar disturbance on the LTE radio access network for mobile services are analysed, and possible countermeasures are presented from the perspective of radiomobile network evolution to 5G and 6G.

A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports

Diminishing viability of manual fault diagnosis in the increasingly complex emerging cellular network has motivated research towards artificial intelligence (AI)-based fault diagnosis using the minimization of drive test (MDT) reports. However, existing AI solutions in the literature remain limited to either diagnosis of faults in a single base station only or the diagnosis of a single fault in a multiple BS scenario. Moreover, lack of robustness to MDT reports spatial sparsity renders these solutions unsuitable for practical deployment. To address this problem, in this paper we present a novel framework named Hybrid Deep Learning-based Root Cause Analysis (HYDRA) that uses a hybrid of convolutional neural networks, extreme gradient boosting, and the MDT data enrichment techniques to diagnose multiple faults in a multiple base station network. Performance evaluation under realistic and extreme settings shows that HYDRA yields an accuracy of 93% and compared to the state-of-the-art fault diagnosis solutions, HYDRA is far more robust to MDT report sparsity.

Rain Effect on 4G LTE In-Car Electromagnetic Propagation Analyzed Through MDT Radio Data Measurement Reported by Mobile Phones

Mobile networks’ evolution is strictly related to connected cars evolution, making in-car propagation modeling and analysis key enablers in the forthcoming telecommunication competition scenario. The recent availability of mobile device power measurements enriched by GPS position, the so-called 3GPP minimization of drive test (MDT) feature, nowadays, empowers new approaches to propagation studies targeting mobile service and quality improvements. The novelty of the research presented in this article is the use of a huge quantity of MDT geolocated radio measurements in a connected state (more than 370 million during the three-day MDT campaign) along 187 km of Italian A22 Motorway, part of the planned Munich-Bologna EU cross-border corridors for connected and automated mobility (CAM). In particular, MDT measurements of LTE received signal received power (RSRP) are compared between rain and dry road situations, showing how wet environments reshape electromagnetic propagation, even for LTE frequency bands (below 3 GHz) that have line-of-sight propagation marginally influenced by rain. Explanations of measured effects and simulations are presented. The rain effect on cellular propagation should be carefully considered in the perspective of connected car evolution, specifically considering new worldwide available 5G bands, more affected by rain situations, and autonomous driving scenarios.

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

In this paper, a commercial high-accuracy, low-cost and real-time indoor building-level localization system is proposed, which is applicable for locating the Minimization of Drive-Tests (MDT) data in the long-term-evolution (LTE) cellular communication network system. The system utilizes MDT data containing Global Navigation Satellite Systems (GNSS) information which is easy to collect and low cost to assist indoor localization, instead of using indoor drive test (DT) data which needs high manual collection and maintenance costs. In order to compensate for the loss of location accuracy, this paper innovatively divide the online process into two phases: indoor and outdoor (IO) identification phase and indoor localization phase. A real-time and precise GMM-based unsupervised algorithm is applied to identifying if the non-GNSS MDT data is in indoor environment in IO identification phase. Then, a multi-class classification algorithm based on Bayesian classifier is used to locate indoor MDT data to the specific building. The results of experiments conducted in an in-service LTE network using more than 100 LTE base stations demonstrate that the proposed technique yields a IO identification accuracy of 90% and an indoor location accuracy of 49.3m(@67%) respectively, which provides at least 30.2% enhancement in location accuracy compared to traditional technology without DT fingerprint database. This proposed indoor localization technique is applicable in network optimization and Operation and Maintenance (O&M) to assist communication service providers to reduce their operating expense (OPEX) by locating those MDT data without GNSS information.

Enhanced MDT-Based Performance Estimation for AI Driven Optimization in Future Cellular Networks

Minimization of drive test (MDT) allows coverage estimation at a base station by leveraging measurement reports gathered at the user equipment (UE) without the need for drive tests. Therefore, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in future cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. We characterize three key types of errors in MDT-based coverage estimation that stem from inaccurate user positioning, scarcity of user reports and quantization. For the first time, the presented analysis shows existence of joint interplay between these errors on coverage estimation that result from inter-dependency between positioning error and bin width. The analysis also shows that there exists an optimal bin width for given user positioning inaccuracy and user density that minimizes the overall error in MDT-based estimated coverage. Utility of our framework is presented by addressing two applications from network optimization perspective: determining optimal bin width to maximize accuracy of MDT-based coverage estimation and its calibration to further improve its accuracy.

An Accuracy and Real-Time Commercial Localization System in LTE Networks

In this paper, an ARTLoc (accuracy and real-time commercial localization) system is proposed, which is applicable for positioning the Minimization of Drive-Tests(MDT) data in the long-term-evolution(LTE) cellular communication network system. This system utilizes a collaborative filtering algorithm to ensure the robustness and accuracy of the fingerprint database when both base station position and GPS MDT data are abnormal in commercial communication service provider(CSP) MDT localization scene. Then, a fast matching KNN algorithm including coarse matching and fine matching is proposed to improve the location efficiency and reduce the location cost under the premise of ensuring high location accuracy. The results of experiments conducted in an in-service LTE network using more than 1000 LTE base station demonstrate that the proposed technique yields a location accuracy of 65.7 meters(@67%) with DT data and 62.1 meters(@67%) with massive GPS MDT data(over 10 million), which provides at least 184.2% and 145.2% enhancement in accuracy respectively compared to the traditional technology. In addition, our system provides a more than 7 times improvement in location efficiency compared to traditional technology. This proposed localization technique is applicable in network optimization and Operation and Maintenance(O&M) to assist CSP to reduce their operating expense(OPEX) by positioning those MDT data without GPS location.

Optimal Bin Width for Autonomous Coverage Estimation Using MDT Reports in the Presence of User Positioning Error

Minimization of drive test (MDT) allows coverage to be estimated at the base station using user equipment measurement reports with the objective of eliminating the need for drive tests. In this letter, we quantify various types of errors in MDT-based autonomous coverage estimation that stem from inaccurate user positioning, for example, as a result of GPS measurement uncertainties and quantization due to dividing the coverage area into bins. By investigating the interplay between quantization and positioning error to estimate coverage, we show that there exists an optimal bin width for coverage estimation and determine it as a function of positioning error and user density. This can enable network operators to configure the bin size for given positioning accuracy that results in the most accurate MDT-based coverage estimation.

Multipath and Doppler Characterization of an Electromagnetic Environment by Massive MDT Measurements From 3G and 4G Mobile Terminals

This paper describes an innovative approach to radio channel characterization in UMTS and LTE mobile networks. In place of traditional drive tests (DT), which employ a single test mobile, a massive collection of georeferenced radio measurements is made from a wide population of user equipment (UE). This is possible with new 3GPP features, called “minimization of DTs” (MDT), which are implemented in the last generation UEs and enable the reporting of additional periodical measurements, including GPS position and estimated UE distance (i.e. delay) over the radio path. This opens to new fields of investigation in the mobile radio channel, unreachable with the legacy DT approach, such as multipath and Doppler analysis. The UE MDT data of UMTS and LTE RAN of Telecom Italia Mobile, in the Italian midsized city of Bologna, have been statistically analyzed. The big data elaboration has been performed with the Nokia proprietary system “GeoSynthesis.” The results give a high-resolution geographical view of the above-mentioned channel phenomena affecting the quality and user experience. They are in good accordance with network performance indicators: the higher the multipath time, the worse the decoding performance of radio blocks (block error rate). The estimated Doppler shift also fits the known mobility patterns in the urban environment.

MDT 데이터를 활용한 심층 신경망 기반의 실내외 구분 기법

본 논문은 LTE (long term evolution) 시스템에서 사용하는 minimization of drive test (MDT) 데이터를 이용한 심층 신경망(deep neural network, DNN) 기반의 실내외 구분 기법을 제안한다. 제안하는 기법은 MDT 데이터 중 실내외 구분에 가장 적합한 기준 신호 수신 전력(reference signal received power, RSRP)을 네트워크의 입력으로 사용한다. 이때 채널 페이딩의 효과를 줄이기 위해 RSRP는 이동 평균 필터에 통과시켜 전처리한 뒤 사용한다. 측정 조건에 따른 RSRP 값의 변동성에 강인한 네트워크를 구성하기 위하여 제안하는 기법은 실내외 상태 변화에 초점을 맞춰 동작하며 현재의 RSRP 값과 함께 과거의 RSRP값을 입력으로 사용한다. 또한 출력은 실내, 실외, 실내에서 실외로 이동, 실외에서 실내로 이동의 네 가지 상태로 구성된다. 실측 데이터를 이용한 실험 결과 제안하는 기법은 96.1%의 추정 정확도를 얻음을 확인하였다.

Data Analytics for Diagnosing the RF Condition in Self-Organizing Networks

The current trend in the management of mobile communication networks is to increase the level of automation in order to enhance network performance while reducing Operational Expenditure (OPEX). In this context, the 3rd Generation Partnership Project (3GPP) has presented different solutions. On the one hand, Self-Organizing Networks (SON) include self-healing capabilities, which allow operators to automate their troubleshooting tasks in order to identify and solve the problems of the network. On the other hand, the use of mobile traces or Minimization of Drive Tests (MDT) are proposed to automate the collection of user's measurements and signalling messages. This paper proposes to combine both solutions, SON and traces, with the purpose of quickly detecting and solving issues related to the radio interface. That is, the user information gathered by the cell traces function is used to perform an automatic diagnosis of the RF condition of each cell. In addition, the proposed approach allows to precisely locate RF problems based on the assessment of the RF condition. Mobile traces constitute large sets of data, whose analysis requires the application of big-data analytics techniques. The proposed system has been evaluated in two different live LTE networks, demonstrating its validity and utility.

How Reliable is MDT-Based Autonomous Coverage Estimation in the Presence of User and BS Positioning Error?

Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self organizing network (SON) feature. MDT allows coverage to be estimated at the base station (BS) using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT-based coverage estimation methods recently proposed in literature assume that UE position is known at the BS with 100% accuracy, an assumption that does not hold in reality. In this letter, we develop an analytical model that allows the quantification of error in MDT-based autonomous coverage estimation (ACE) as a function of error in UE as well as BS positioning. Our model also allows characterization of error in ACE as function of standard deviation of shadowing.

Fixed Rank Kriging for Cellular Coverage Analysis

Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements that are gathered today during highly expensive drive tests (DT) and will be reported in the near future by users' mobile devices thanks to the Third-Generation Partnership Project (3GPP) minimization of drive tests (MDT) feature. This feature consists of an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: It builds a coverage map by spatially interpolating geo-located measurements using the Kriging technique. This paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying fixed rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good tradeoff between prediction efficiency and computational complexity. In order to go a step further toward the operational application of the proposed algorithm, a multicellular use case is studied. Simulation results show good performance in terms of coverage prediction and detection of the best serving cell.

Automate Minimization of Drive Tests for QoE Provisioning: The Case of Coverage Mapping

The concept of Minimization of Drive Tests (MDT) has been developed in Third Generation Partnership Project (3GPP) specifications standard. With MDT, Mobile Network Operators (MNOs) were enabled to remotely collect measurements indicating the network Quality of Service (QoS), as experienced by their users, correlated with actual location information. This results in wider application of use cases that allows network monitoring and optimization, without the need for conventional drive tests. To facilitate access to distributed geospatial data through a set of policies, common rules, and standards that would improve interoperability, this paper, proposes an open architecture, in compliance with the Open Geospatial Consortium (OGC) web services and the MDT architectures, enabling the automation of the MDT use cases and real-time service management.