Mobile Terminal Location Research Articles

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

Link-Level Simulator for 5G Localization

Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks. However, there is no unified and effective platform to support the research on 5G localization algorithms. This paper releases a link-level simulator for 5G localization, which can depict realistic physical behaviors of the 5G positioning signal transmission. Specifically, we first develop a simulation architecture considering more elaborate parameter configuration and physical-layer processing. The architecture supports the link modeling at sub-6GHz and millimeter-wave (mmWave) frequency bands. Subsequently, the critical physical-layer components that determine the localization performance are designed and integrated. In particular, a lightweight new-radio channel model and hardware impairment functions that significantly limit the parameter estimation accuracy are developed. Finally, we present three application cases to evaluate the simulator, i.e. two-dimensional mobile terminal localization, mmWave beam sweeping, and beamforming-based angle estimation. The numerical results in the application cases present the performance diversity of localization algorithms in various impairment conditions.

Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks

5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile network operators and cellular users worldwide. The rollout process of a new capacity layer (cell) on top of the existing previous cellular technologies is a complex process that requires time and manual effort from radio planning-engineering teams and parameter optimization teams. When it comes to optimum configuration of the 5G gNB cell parameters, the maximization of achieved coverage (RSRP) and quality (SINR) of the served mobile terminals are of high importance for achieving the very high data transmission rates expected in 5G. This process strongly relies on network measurements that can be even more insightful when mobile terminal localization information is present. This information can be generated by modern algorithmic techniques that act on the cellular network signaling measurements. Configuration algorithms can then use these measurements combined with location information to optimize various cell deployment parameters such as cell azimuth. Furthermore, data-driven approaches are shown in the literature to outperform traditional, model-based algorithms as they can automate the optimization of parameters while specializing in the characteristics of each individual geographical zone. In the context of the above, in this paper, we tested the automated network reconfiguration schemes based on unsupervised learning and applied statistics for cell azimuth steering. We compared network metric clustering and geospatial clustering to be used as our baseline algorithms that are based on K-means with the proposed scheme—hybrid network and spatial clustering based on hierarchical DBSCAN. Each of these algorithms used data generated by an initial scenario to produce cell re-configuration actions and their performance was then evaluated on a validated simulation platform to capture the impact of each set of gNB reconfiguration actions. Our performance evaluation methodology was based on statistical distribution analysis for RSRP and SINR metrics for the reference scenario as well as for each reconfiguration scheme. It is shown that while both baseline algorithms improved the overall performance of the network, the proposed hybrid network–spatial scheme greatly outperformed them in all statistical criteria that were evaluated, making it a better candidate for the optimization of 5G capacity layers in modern urban environments.

Time of Arrival Ranging and Localization Algorithm in Multi-Path and Non-Line-of-Sight Environments in OFDM System

Time of Arrival (TOA) is a widely used wireless cellular network ranging technology. How to perform accurate TOA estimation in multi-path and non-line-of-sight (NLOS) environments and then accurately calculating mobile terminal locations are two critical issues in positioning research. For NLOS identification, it can be performed in the TOA measurement part and the position calculation part, respectively. In this paper, for the above two steps, two schemes for mitigating NLOS errors are proposed. First of all, a TOA ranging method based on clustering theory is proposed to solve the problem of line of sight (LOS) path estimation in multi-path channels. We model TOA ranging as a Gaussian mixture model and illustrate how LOS and NLOS can be measured and identified based on non-parametric Bayesian methods when the wireless transmission environment is unknown. Moreover, for NLOS propagation channels, this paper proposes a user location estimator based on the maximum a posteriori (MAP) criterion. Combined with the TOA estimation and user location computation scheme proposed in this paper, the terminal's positioning accuracy is improved. Experiments show that the TOA measurement and localization algorithms presented in this paper have good robustness in complex wireless environments.

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

Indoor positioning technology plays an important role in the production and life of today's society. Modern people's clothing, food, housing, and transportation are increasingly closely related to Location Based Service. In this paper, an indoor location algorithm based on the radio frequency fingerprint matching is offered. In the offline phase, by deploying a small number of radio signal intensity location regions, the location selection mechanism is used to predict the location of mobile terminals, which can avoid the instability of radio frequency signals caused by time-lapse and the impact of changes in Access Point (AP) facilities. In the online stage, the location engine receives real-time Radio Frequency fingerprints and updates the fingerprint location by mapping time and equipment. Finally, several main localization algorithms are simulated and compared to the aspect of location accuracy. The proposed new method reduces the location error, improves the location accuracy by 1 to 2 meters, and achieves 100% within 2.5 meters.

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

SummaryRecent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.

Detection of the First Component of the Received LTE Signal in the OTDoA Method

In a modern world there is a growing demand for localization services of various kinds. Position estimation can be realized via cellular networks, especially in the currently widely deployed LTE (Long Term Evolution) networks. However, it is not an easy task in harsh propagation conditions which often occur in dense urban environments. Recently, time-methods of terminal localization within the network have been the focus of attention, with the OTDoA (Observed Time Difference of Arrival) method in particular. One of the main factors influencing the accuracy of location estimation in the OTDoA method is the nature of the propagation channel that affects the ease of isolating the signal component travelling from the transmitter to the receiver through the shortest path. To obtain the smallest possible localization error, it is necessary to detect the first received component of the useful signal. This aim could be achieved by using a proper algorithm within the receiver. This paper proposes a new algorithm for effective detecting of the first component of the LTE downlink signal in the multipath environment. In a mobile terminal location estimation process, CSRS (Cell Specific Reference Signal) signals were used instead of dedicated PRS (Positioning Reference Signal) signals. New solution was verified during the measurement campaign in a real LTE network.

An RSS-based regression model for user equipment location in cellular networks using machine learning

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods.

Important Location Identification and Personal Location Inference Based on Mobile Subscriber Location Data Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings

As an emerging spatial trajectory data, mobile terminal location data can be widely used to analyze the behavior characteristics and interests of individuals or groups in smart cities, transportation planning and other civil fields. It can also be used to track suspects in anti-terrorism security and public opinion management. Aiming at the problem that it is difficult to determine suitable input parameters of clustering caused by different subscriber location data size and distribution difference, an improved density peak clustering algorithm is proposed and the performance of the improved algorithm is verified on the UCI data set. Firstly the important location is identified by the proposed algorithm, and the personal location is further inferred by the algorithm based on the subscriber's schedule and maximum cluster. Then, the algorithm adopts Google's inverse geocoding technology to obtain the semantic names corresponding to the coordinate points, and introduces the natural language processing technology to achieve word frequency statistics and keyword extraction. The simulation results based on the Geolife data set show that the algorithm is feasible for identifying important locations and inferring personal locations.

Method Based on Confidence Radius to Adjust the Location of Mobile Terminals

The present paper details a technique for adjusting in a smart manner the position estimates of any user equipment given by different geolocation/positioning methods in a wireless radiofrequency communication network based on different strategies (observed time difference of arrival , angle of arrivals, propagation delay$$\ldots$$ź). The main advantage of our proposal is to improve in a remarkable way the accuracy and to mitigate the adverse effect of multipath and other sources of errors that induce to inaccuracy in the terminals position estimates. Accordingly, the estimation of the geographical positions associated to all reported mobile terminals will be remarkable improved independent on the geolocation technique employed. The proposed method will move each position estimate towards a previously calculated area of confidence in a smart manner. This reduced area of confidence is generated to guarantee that the real position of any mobile terminal is inside it with a $$95\,\%$$95% of probability of certainty.

Semantic wireless localization of WiFi terminals in smart buildings

AbstractThe wireless localization of mobile terminals in indoor scenarios by means of a semantic interpretation of the environment is addressed in this work. A training‐less approach based on the real‐time calibration of a simple path loss model is proposed which combines (i) the received signal strength information measured by the wireless terminal and (ii) the topological features of the localization domain. A customized evolutionary optimization technique has been designed to estimate the optimal target position that fits the complex wireless indoor propagation and the semantic target‐environment relation, as well. The proposed approach is experimentally validated in a real building area where the available WiFi network is opportunistically exploited for data collection. The presented results point out a reduction of the localization error obtained with the introduction of a very simple semantic interpretation of the considered scenario.

Stochastic Geometry Modeling and System-Level Analysis & Optimization of Relay-Aided Downlink Cellular Networks

In this paper, a tractable mathematical framework for the analysis and optimization of two-hop relay-aided cellular networks is introduced. The proposed approach leverages stochastic geometry for system-level analysis, by modeling the locations of base stations, relay nodes and mobile terminals as points of homogeneous Poisson point processes. A flexible cell association and relay-aided transmission protocol based on the best biased average received power are considered. Computationally tractable integrals and closed-form expressions for coverage and rate are provided, and the performance trends of relay-aided cellular networks are identified. It is shown that coverage and rate highly depend on the path-loss exponents of one- and two-hop links. In the interference-limited regime, in particular, it is shown that, if the system is not adequately designed, the presence of relay nodes may provide negligible performance gains. By capitalizing on the proposed mathematical framework, a system-level and interference-aware optimization criterion of the bias coefficients is proposed. Numerical results confirm the effectiveness of the proposed system-level optimization to enhance the coverage probability in the interference-limited regime. The presence of relays, on the other hand, is shown to have a limited impact on average/coverage rate under the same assumptions.

Uplink Spectral Efficiency Analysis of In-Building Distributed Antenna Systems

Providing high data rate wireless transmissions has been difficult in indoor environments, particularly in multi-floor buildings. One way to achieve high data rate wireless transmissions is to reduce the radio transmission distance between the transmitter and the receiver by using distributed antenna systems (DASs) and employing frequency reuse. However, due to the reuse of the limited available spectrum, co-channel interference can severely degrade system capacity. In this paper, the uplink spectral efficiency of an in-building DAS with frequency reuse is studied, where remote antenna units (RAUs) deployed on each floor throughout the building are connected to a central unit (CU) where received signals are processed. The impact of co-channel interference on system performance is investigated by using a propagation channel model derived from multi-floor, in-building measurement results. The proposed scheme exploits the penetration loss of the signal through the floors, resulting in frequency reuse in spatially separated floors, which increases system spectral efficiency and also reduces co-channel interference. A comparative analysis with conventional co-located antenna deployment at the floor center is provided. Location based RAU selection and deployment options are investigated. System performance is evaluated in terms of location-specific spectral efficiency for a range of potential mobile terminal (MT) locations and various in-building propagation characteristics.

An Indoor Mobile Location Estimator in Mixed Line of Sight/Non-Line of Sight Environments Using Replacement Modified Hidden Markov Models and an Interacting Multiple Model.

Localization as a technique to solve the complex and challenging problems besetting line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions has recently attracted considerable attention in the wireless sensor network field. This paper proposes a strategy for eliminating NLOS localization errors during calculation of the location of mobile terminals (MTs) in unfamiliar indoor environments. In order to improve the hidden Markov model (HMM), we propose two modified algorithms, namely, modified HMM (M-HMM) and replacement modified HMM (RM-HMM). Further, a hybrid localization algorithm that combines HMM with an interacting multiple model (IMM) is proposed to represent the velocity of mobile nodes. This velocity model is divided into a high-speed and a low-speed model, which means the nodes move at different speeds following the same mobility pattern. Each moving node continually switches its state based on its probability. Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms. Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM. The simulations also show that the three algorithms are accurate, stable, and robust.

A Distributed UWB-based Localization System in Underground Mines

The location of people, mobile terminals and equipment is highly desirable for operational enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflection, diffraction and diffusion on the rough sidewall surfaces are the main sources of range measurement errors. In this paper, a UWB time of flight based localization system is proposed to address the multipath effect in underground mines. To reduce the communication cost and time delay of localization in such a chain type wireless network, a distributed localization algorithm based on particle swarm optimization (PSO) is proposed and implemented on the blind node (the node to be localized). Without extra hardware needed, an accurate but low cost localization system has been achieved. Experimental results verify the proposed scheme

The Metric and Cache-Based (MC-B) System for Location Management in Wireless Cellular Networks

In this paper, we proposed an efficient metric and cache based location management technique to identify the current location of mobile terminals. The attributes of this techniques are, metrics calculation, information exchange, optimal routing path selection and it's storage in the mobile switching center. For this, we introduced a novel architecture for mobile switching center, and it has several cache spaces. First, the mobile switching center will calculate the shortest metric-based path between current and master/previous location of mobile terminal and store its in the cache of the mobile switching center, thereafter it performs the location management procedure through the optimal suggested path by the mobile switching center. The analytical model checks the scalability and effectiveness of the proposed system over certain attributes and the comparison have been made with the existing techniques. It has been observed that the proposed technique reduced the signaling cost, registration delay, call setup delay, network overheads and total location management cost.

The effect of police cruiser restraint cage configuration on shoulder discomfort, muscular demands, upper limb postures, and task performance during simulated police patrol

Advances in police-specific technology have led to changes in work layout and physical occupational demands of mobile police officers. This study investigated the influence of police cruiser compartment configuration on perceived discomfort, muscle activation, shoulder kinematics, and typing performance during simulated police patrol. Participants completed a one-hour session including simulated driving and 2-min typing trials in a standard compartment configuration with a fixed mobile data terminal (MDT) location (ST), and in a modified compartment configuration with an MDT in front of the user and a rearward translated seat (MOD). The MOD configuration resulted in reductions of 55–65% in perceived shoulder discomfort, up to 3.4% MVC in shoulder muscle demands, and more neutral humeral orientations (shoulder elevation reduced by 13–25°). These improvements associated with the MOD configuration may have ergonomic implications for future police car designs, particularly as new technology is introduced in the mobile environment and advanced solutions are sought.

A Selective Paging Scheme Based on Activity in Cellular Mobile Networks for Reporting Centre

The foremost aim of cellular mobile communication is to find out existing location of mobile terminals to give out the service, which is well-known as location management. The LM involves tracking of (mobile terminal's) MT's up-to-date location, which moves freely across different cells in order to provide them services. Every MT undergoes same number of updates when passes through a definite re gion. One such scheme is reporting centre in which, some of the cells are designated as reporting centres and all close by cells up to the next reporting centre belong to vicinity of same reporting centre. MT updates its location whenever it crosses vicinity of its current reporting centre, which happens no more than when it enters into another reporting centre and therefore a LU is triggered. To deliver a call, network pages current reporting centre and its entire vicinity simultaneously to locate the targ et MT. We have applied predictionbased selective paging on reporting centre scheme in cellular mobile networks, which reduces paging cost without affecting the location update cost. Paging cost by the side of with LM cost for both the conventional and proposed schemes will be updated as a consequence which gives the results .

파티클군집최적화 방법을 적용한 위치관리시스템 최적 설계

Location area planning (LAP) problem is to partition the cellular/mobile network into location areas with the objective of minimizing the total cost in location management. The minimum cost has two components namely location update cost and searching cost. Location update cost is incurred when the user changes itself from one location area to another in the network. The searching cost incurred when a call arrives, the search is done only in the location area to find the user. Hence, it is important to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking cost is a minimum. The complete mobile network is divided into location areas. Each location area consists of a group of cells. This partitioning problem is a difficult combinatorial optimization problem. In this paper, we use particle swarm optimization (PSO) to obtain the best/optimal group of cells for 16, 36, 49, and 64 cells network. Experimental studies illustrate that PSO is more efficient and surpasses those of precious studies for these benchmarking problems.

Evaluation of the influence of mobile data terminal location on physical exposures during simulated police patrol activities

Prolonged occupational police driving combined with use of an in-vehicle computer elicits awkward, sustained postures in a scenario that lacks the adjustability to accommodate many mobile officer anthropometries and job-specific components.Twenty participants performed simulated police patrol sessions at five mobile data terminal (MDT) locations and using two seats: standard police vehicle seat and modified seat designed for police use.An MDT location self-selected prior to the session reduced perceived discomfort by up to 50% in the low back (p < .0001) and 68% in the right shoulder (p < .0001) compared to other tested locations, including the most common currently used location recorded from a representative police force. Muscle activity was lowest at the self-selected and current MDT locations for all muscles, significantly so for posterior deltoid (p < .0001) and supraspinatus (p < .0001). The modified seat reduced low back discomfort from the standard seat by 28% (p < .0001). Combining a self-selected MDT location and modified driver seat generated lower discomfort and physical loading than the currently used configuration.