Mobile Station Location Research Articles

Mobile station localization based on hybrid SADOA/AOA in cellular networks

Mobile station localization based on hybrid SADOA/AOA in cellular networks

Artificial Intelligence Techniques for Mobile Station Location Estimation

Modern wireless communication systems require positioning functions, which provide are automatic location estimation of stations within a network. However, when new networks are implemented, much higher accuracy is required when determining geographical coordinates of a mobile station to develop of services related to the station location. To solve the problem of mobile station positioning, its geographical coordinates are calculated, coordinates of the closest base stations being known. The paper proposes to use a genetic neuro-fuzzy controller for improving the effectiveness of positioning a mobile station. Positioning methods providing usage of artificial intelligence methods are based on measurements of levels for signals from the closets access points or base stations, their coordinates are known. The proposed localization method is based on values of received signal strength indicator – RSSI. At the same time, the RSSI method has a disadvantage – low accuracy, which is proposed to be increased by applying methods of artificial intelligence – fuzzy logic, neural networks, genetic algorithms. Therefore, the objective of this paper is to elaborate an optimized method for determining location of a mobile station. In compliance with the suggested method, RSSI values and ToA values enter the genetic neuro-fuzzy controller, after corresponding processing, the distance from the mobile station to the base station appears at its output.

Research on Improved PSO in Communication Site Planning

Aiming at the planning problem of mobile communication station location, this study first uses the base station distribution data to screen, and on this basis, establishes the objective function and constraint conditions of station number and station location positioning. Secondly, based on the basic particle swarm optimization algorithm and Yangwei particle swarm optimization algorithm, the original algorithm is optimized by adding inertia weight disturbance to obtain the optimal number of station sites and station location, and then the station location is clustered. It is concluded that the establishment of 455 macro base stations and 1902 micro base stations is the most suitable, and the weak coverage base stations are divided into 35 categories for management.

Cellular Positioning in an NLOS Environment Applying the COPSO-TVAC Algorithm

Non-Line-of-Sight (NLOS) conditions are created by blocking the direct path between the transmitter and receiver, resulting in an increased signal propagation path. To mitigate the Time of Arrival (TOA) measured errors caused by the NLOS phenomenon in cellular radio positioning, we use the Maximum Likelihood (ML) estimation method in this work. The cost function of the ML estimator is usually a high-dimensional, nonlinear, and multimodal function, where standard deterministic optimization techniques cannot solve such problems in real-time and without significant computing resources. In this paper, effective metaheuristic algorithms based on the enhanced variants of Particle Swarm Optimization (PSO) are applied for the optimal solution of the ML problem and efficiently determine the mobile station location. Time-Varying Acceleration Coefficients (TVAC) are introduced into the standard PSO algorithm to enhance the global search and convergence properties. The resulting algorithm is known as PSO-TVAC. To further improve the performance of the metaheuristic optimization, we suggest adding Chaos Search (CS), Opposition-Based Learning (OBL), and TVAC strategy to the PSO process. The simulation results show that the proposed metaheuristic algorithm named the Chaotic Opposition-based PSO-TVAC (COPSO-TVAC) can reach the Generalized Cramer–Rao Lower Bound (GCRLB) and surpass the original PSO, PSO-TVAC, and the presented conventional optimization algorithms.

High mobility transmission system under intelligent reflecting surface

AbstractIn wireless communication system with a high mobility scenario, there is a challenge to obtain fast data transmission service with proper stability. Also, it is difficult to obtain perfect channel state information in the high mobility case without large online computation. Intelligent surface has great potential to cost‐effectively enhance the system performance in next generation technology and beyond. In this paper, a railway communication system with the help of intelligent surface technology is proposed for high mobility scenario. We propose an applicable beamforming scheme with the help of mobile station location information for intelligent surface‐aided communication system under high mobility. In our proposed system, the beams from the intelligent surface are chosen based on the mobile stations locations, where the weights of the beams are optimized for maximum total service of base station. Also, we formulate the optimization problem and propose an alternating iterative technique as a solution. The weights and phase matrix of the beams are optimized to maximize the mobile service. An intelligent surface with beam selection scheme is also proposed. Results demonstrate the significant mobile service enhancement introduced by the intelligent surface in railway communication system and show the effectiveness of our proposed scheme. It can reduce the handover region and can save time for data transmission.

Low power hardware design and its mathematical modeling for fast‐exact geolocalization system in wireless networks

SummaryA significant goal in achieving position calculation is to reduce and simplify the instructions that the Mobile Station (MS) has to accomplish in the location determination algorithm. Standard techniques widely used in many applications can be classified into Received Signal Strength Indicator (RSSI), Angle of Arrival (AoA), Time Difference of Arrival (TDoA), and Time of Arrival (ToA). However, all are almost determined by the standard trigonometric complex computation methods that are usually implemented in software where the complexity is considerable. The development of a simple and efficient technique for MS location estimation is the main objective of this paper. To design a modern hardware‐oriented algorithm, a new approach using simple Add‐and‐Shift operations in the computation is proposed and it can be implemented in a hardware target. In order to accelerate the geopositioning process, the Coordinate Rotation Digital Computer (CORDIC) algorithm has been used in this proposal. The proposed algorithm is simpler and faster than conventional ones; thus, complexity in terms of time, power consumption, and chip area has been reduced. Therefore, the algorithm is suitable for applications with limited computational and power resources. Based on comparisons with the state‐of‐the‐art algorithms, it is concluded that the proposed technique is more exact and less complex.

Analysis of greedy algorithm based base station siting search optimization

With the rapid development of 5G technology, the bandwidth of communication is increasing while the coverage of base stations is decreasing. Choosing a site for a mobile network, taking into account the coverage and cost of the base station, is a very challenging issue. When an area has weak coverage, it has an impact not only on the quality of the network, but also on the user experience. Therefore, the clustering of weak coverage points has to be continuously enhanced to facilitate the continuous development of mobile communication technology. This paper firstly establishes the optimization objective of finding large coverage with low station construction cost. Secondly, a greedy algorithm is used to calculate the optimal station site and the optimal sector angle to maximize coverage, taking into account two objective functions: cost and coverage, and to give an optimization scheme for the location of mobile communication network base stations.

Projective Approach in Determining Homogeneous Hyperspherical Geometrically-Based Stochastic Channel Model’s Statistics: Angle of Departure, Angle of Arrival and Time of Arrival

This article proposes geometrically-based stochastic channel model with scatterers homogeneously distributed within $N$ -dimensional ( $N$ -D) hyperspherical-shaped scattering region for single-bounce propagation scenario, with arbitrary positions of base station (BS) and mobile station (MS). For such defined geometrically-based stochastic channel model, the angular and temporal statistics are determined by introducing the projective approach. Accordingly, azimuthal angle and time of arrival marginal PDFs are derived in closed form, while the elevation angle PDF can be delivered numerically in general, and in closed-form for specific environmental parameters. The fidelity of the analytically obtained results is evaluated by their comparison to the corresponding normalized histograms. Also, it is shown that the proposed $N$ -D model can be used to analyze some of the existing channel models like 2-D uniform disk and 3-D uniform (hemi)sphere models. Additionally, by introducing the mentioned projective approach, it is shown that the angular statistics of the proposed $N$ -D model are the same as the angular statistics of some nonuniform 2-D and 3-D models, which is an important property of the proposed model. Such observation enabled us, for the first time in the literature, to determinate angular statistics for geometrically-based stochastic channel models such as inverted parabolic scattering model, 2-D Gaussian model and 3-D Gaussian hemisphere model, for arbitrary positions of BS and MS. Such angular characteristics of proposed channel model are validated through several empirical datasets.

Position-Aided Beam Learning for Initial Access in mmWave MIMO Cellular Networks

In this article, beam learning based on position information (PI) about mobile station positions in the initial access (IA) of millimeter wave (mmWave) multiple-input–multiple-output (MIMO) cellular networks is investigated. The existing PI-based IA procedure cannot efficiently tackle the position inaccuracy and blockage or may cause a long IA delay because of the inefficient beam learning. Based on the sparse scattering of mmWave signals, the serving area is partitioned into smaller areas and the beams are learned for each small area. Moreover, the number of learned beams is restricted and fixed after learning. Thus, the impact of position inaccuracy and blockage can be mostly mitigated and the IA delay is short in each successful IA. The analysis shows the lower bound of the probability of miss detection. Additionally, the simulation results show that the proposed approach can achieve a reasonable IA delay and superior IA performance than other PI-based approaches.

An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks

An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks

MS Location Estimation Based on the Artificial Bee Colony Algorithm.

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.

An Enhanced-Received Signal Strength Technique for Estimating Mobile Station Position in Wireless Sensor Networks

An Enhanced-Received Signal Strength Technique for Estimating Mobile Station Position in Wireless Sensor Networks

Greedy Random Walk Scheme Based MSL Privacy for WSN

The privacy for station-locations of WSN is a key problem in battlefield. Being exposed for location, the consequences are unthinkable. RDCS (Random Data Collection Scheme) has a problem that message latencies become higher. In this paper, GRWS (Greedy Random Walk Scheme) is proposed to preserve MSL (Mobile-Station-Location) privacy. In GRWS, data are forwarded and stored at pass nodes, the station move to get data sporadically, which prevents the attackers from predicting their locations and movements. Compared to RDCS, GRWS has smaller message latencies, while providing satisfactory MSL privacy.

The Communication System of Building from Outdoor to Indoor with AMC at 10 GHz

The propagation model of communication systems was used propagation from outdoor to indoor of building. In the inside that building used partition with brick. That propagation condition used downlink condition from mobile station side. The communication frequency used 10 GHz. Some parameter variation was used in this research such as radio base station coverage, mobile station location of building, and code rate communication. The coverage variation of radio base station used femtocell and picocell. As the result described signal to noise ratio (SNR)at every node communication, adaptive modulation and coding (AMC) variation, and coverage area percentage in the building. The modulation and coding scheme (MCS) was used consist of QPSK, 16 QAM, and 64 QAM

Distinguish Potential Source Areas of PM2.5 and PM10 by Statistical Data Analysis

Power plant from biomass in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air. For air pollution, total suspended particulate (TSP) and particulate matter less than 10 micron (PM10) from rice husk energy process is quite large. Roi Et is a province located in Northeast of Thailand which obtains power plants using rice husk as fuel in the process. According to the land-use pattern of the community area of Roi Et, it revealed that there are many sources of air pollution. This study selected the statistical techniques to distinguish the potential source area influencing the high concentrations of particulate matter less than 2.5 micron (PM2.5), particulate matter 2.5 to 10 micron (PMcoarse), and PM10 which caused the health effects of people around the rice husk power plants. Hourly observed PM10 and PM2.5 data were analysed by time series and bivariate polar plot. Results showed that increasing the concentration of PM10 and PM2.5 were observed in the duration of low wind speed. However, daily average of PM10 was not more than National Ambient Air Quality Standard (NAAQS) which is 120 μg/m3. In contrast, results showed that daily average of PM2.5 performed over NAAQS of 50 μg/m3 in the 20th -22nd December 2018 that high pressure cover Thailand. While sources of these particulate matters could be distinguished into 3 potential areas based on the mobile air quality monitoring station location which were 1) east side and 2) north-west side which regarded as sub-urban and traffic areas, respectively and 3) north-east side which regarded as power plants area. Results revealed that PMcoarse was the major pollutant dispersed from the Northeast direction that the power plants were the major source. Whereas traffic and community were the main sources of PM2.5 coming from the East and the Northwest direction of the mobile air quality monitoring station point. Therefore, discrimination of each point source could be done first before implementing to control and manage the air pollution effectively.

Biogeography Based Optimization for Mobile Station Reporting Cell System Design

Fast service access involves keeping track of the location of mobile users, while they are moving around the mobile network for a satisfactory level of QoS (Quality of Service) in a cost-effective manner. The location databases are used to keep track of Mobile Terminals (MT) so that incoming calls can be directed to requested mobile terminals at all times. MT reporting cell system used in location management is to designate each cell in the network as a reporting cell or a non-reporting cell. Determination of an optimal number of reporting cells (or reporting cell configuration) for a given network is reporting cell planning (RCP) problem. This is a difficult combinatorial optimization problem which has an exponential complexity. We can see that a cell in a network is either a reporting cell or a non-reporting cell. Hence, for a given network with N cells, the number of possible solutions is  . We propose a biogeography based optimization (BBO) for design of mobile station location management system in wireless communication network. The number and locations of reporting cells should be determined to balance the registration for location update and paging operations for search the mobile stations to minimize the cost of system. Experimental results show that our proposed BBO is a fairly effective and competitive approach with respect to solution quality for optimally designing location management system because BBO is suitable for combinatorial optimization and multi-functional problems.

ЗАСТОСУВАННЯ НЕЙРОННОЇ МЕРЕЖІ У ПРОЦЕДУРІ ВЕРТИКАЛЬНОГО ХЕНДОВЕРУ

The deployment of modern heterogeneous networks transmitting different types of traffic from mobile stations, which move at a quite high speed, has led to increased requirements for the quality of vertical handover operation. The handover procedure or handover is a key mechanism that allows mobile subscribers to move seamlessly across the network. At the same time, due to the influence of a large number of parameters and characteristics of the network, traditional decision-making schemes that work with only one criterion are ineffective. Therefore, there is a task to develop a handover mechanism taking into account several parameters and providing the intelligent handover. One of the promising fields of modern technology is artificial neural networks. An important feature of neural networks is the parallel processing of information by a large number of neurons at the same time. Typical tasks that can be solved using neural networks are: control, encoding and decoding of information, classification, forecasting, automation of a decision-making process, pattern recognition and more. Neural networks are used in wireless communication systems to solve the following tasks: access control, handover, channel allocation, traffic forecasting, adaptive routing, signal propagation prediction, mobile station location. The paper proposes to utilize a neural network in the intelligent algorithm of multicriterial vertical handover. After creating a software or hardware solution for the neural network, a mathematical model must be created and the network training must be performed. The neural network proposed in this paper is a multilayer perceptron. Simulation of the neural network operation in Matlab program was performed.

Weighted Multiple Linear Regression Model for Mobile Location Estimation in GSM Network

The numbers of crimes and accidents, among other challenging issues, requiring a mobile application with localization capabilities are on the increase. Yet there is under-utilization of location information provided by mobile phones. The accuracy and cost of implementation of mobile position localization on cellular network have been an issue of research interest. In this paper, the statistical modelling of mobile station (MS) position location was carried out using weighted multiple linear regressions (WMLR) method. The proposed statistical modelling approach was based on received signal strength (RSS) technique. The model improved localization accuracy. The model's simulated results were analysed and compared with the existing MLR using real measured data collected from GSM network in a light urban environment in Enugu, Nigeria.

Application of Small Mobile Ground Station in UAV Location

Aiming at the problem that UAV can’t locate accurately in the areas where the satellite navigation signal is poor or even lost, this paper proposes to establish a mobile ground station positioning system to realize the precise positioning of UAV in such areas. Mobile ground station positioning system is a new positioning concept, as a supplement to satellite navigation and positioning system, and breakthrough to the existing ground-based reinforcement system fixed ground station, has greatly expanded the area and type of UAV mission execution, and further promoted the application of UAV in mission execution.

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.