Mobile Positioning Techniques in GSM Cellular Networks based on Signal Strength

Transcription

1 Mobile Positioning Techniques in GSM Cellular Networks based on Signal Strength Mr. G.V.V.S.N. Babji, E. Nageswara Rao M.Tech Scholar, Prof & HOD, Abstract - Locating the site of a mobile user with a high degree of correctness is a research interest that holds the key to a get through in many service challenges faced by operators in the wireless communication world. The benefits of a success in this field ranges from value added services and efficient advertising to crime detection and fighting. Many technologies have been developed which made use of different algorithms to provide answers to these challenges but with varying degrees of accuracy, operational challenges, varying ease of deployment and cost of installation. Mobile position estimation technologies use techniques such as Time Difference of Arrival (TDOA), Observed Time Difference of Arrival (OTD), Enhanced Observed Time Difference of Arrival (E -OTD), Angle of Arrival (AOA), Time of Arrival, Received Signal Strength (RSS) indication, GPS systems, and Cell ID. It is the focus of this paper to analyze the performance of these techniques using different performance indices and then relatively compare the results available from these indices. Index Terms - Angle of Arrival (AOA), Observed Time Difference of Arrival (OTD), Signal Strength, Network Measurement Report (NMR), Finger Printing, Multipath, Base Transceiver Station (BTS) I. INTRODUCTION To identify the reliability of the techniques discussed in this paper, experiments were approved in real mobile network environment. Experiments were also carried out to find the effect on the mobile signal in different speed and the effect of speed on the accuracy of the MS location. In this paper we also discuss the possible ways to increase the accuracy of the results. Ali, O., Ali, S. and Koray, C., [1] have ECE Department, RIT-Yanam, UT of Puducherry demonstrated the GPS and GSM application for Interactive location and speed tracking. Whereas Ananth, R., [2] enabled location handsets that gave rise to new and diverse mobile workforce management applications. U.S. Federal Communications Commission (FCC) has made it mandatory that wireless emergency (E -911) callers must be located with an accuracy of 300m (CC Docket 1994)[3]. Mobile locations could support many applications, such as emergency services, roadside assistance, billing, navigation, tracking, and so on [4]. The first category GPS has been standardized in GSM (Global System for Mobile Communications) networks as the Assisted-GPS method [5], which requires hardware development in both the mobile terminal and the network. The second category locates the mobile via the angle-ofarrival (AOA) of a signal from the mobile at several BSs [6]. This technique requires no time synchronization between stations and can work with as few as two BSs. The third category estimates the mobile location based on the Received Signal Strength Indication (RSSI) measurements [7]. This approach uses the relationship between the received signal strength and the distance from the base stations (BS) to the mobile station. They transformed the nonlinear equations relating the RSS to the mobile position to a set of linear equations, which are solved by constrained, weighted least squares. The accuracy achieved by using RSS measurements may not meet all emergency and strategic requirements but it is an attractive solution for less demanding LBS [8]. In another work for mobile location estimation, Yamamoto et al., (2001) [9] considered the fluctuations of RSS due to shadowing and obtained the probability density function of the propagation distance. It is reported that the algorithm is more accurate than the conventional cell-based algorithm. Page 1229

2 Salcic (1997) [10] presented a n automatic GSMbased positioning and communication system for positioning of the mobile station in general way without offering any technical details. Kyriazkos, Drakoulis &Theologou, (2001) [11] Presented a technique based on GPS and network-based technique but with very limited technical details. Several other recent papers also dealt about location based services without offering many technical details [12-13]. Mobile location techniques are either based on the signal transmitted between satellites in the orbit and a mobile device on the earth, the most prominent of which is the Global Positioning System (GPS), or they are network-based in which case the signal transmitted between the cell phone and the network is used for the positioning, or handset-based in which case the handset gets all the parameters it needs from the network and uses it to determine its own position.[3] Satellite-based location systems, often called satellite navigation systems are used extensively in military and commercial applications, such as vehicle tracking, navigation, and clock synchronization. These so called network-based location systems are used in order to avoid the necessity to integrate GPS hardware or to serve as fall-back systems in locations where GPS is not available. This paper makes a comparative and analytic study of the various techniques for mobile positioning and presents the limitations of each and the performance of each method based on some performance indices that will be used in the analysis. II. PERFORMANCE INDICES FOR MOBILE LOCATION TECHNIQUES The comparative analysis of the techniques will be done under the following matrices: Reliability: simply put is the mean time between failures and the mean time to repair. Generally a uniform measurement for reliability is difficult to define due to the variations in technologies. In GPS systems, reliability is defined as a measure of how consistent a GPS horizontal error levels can be maintained below a specified reliability threshold. In mobile positioning methods, we can simply define reliability as the ratio of successful positioning attempts out of all attempts made. Systems used in personal positioning should uncompromisingly give extremely reliable performance especially in emergency cases since failure could be very detrimental. This is an important indicator of performance because a positioning device that frequently fails or gives false positions will not be seen as trustworthy by the users [4]. Latency: Latency measure is basically the time from power-up to the instant when the first location measurement is obtained. In GPS, this is known as the time-to-firstfix (TTFF). The high demand for low or short latency is not only realized in the emergency cases but also from the LBS point of view. Example, the QoS and usability of guidance and tracking applications decreases if one cannot guarantee a real-time operation. Short latency also saves power. It is measured in seconds. Latency is further broken down into three main components by defining the call setup delay, network delay and the processing delay. Briefly, call set-up delay is the time elapsed between call initiation at the MS and receiving the first response from the network. Network delay is defined as the time needed to transmit all messages excluding call set-up delay. Processing delay as the name implies is the time needed by the positioning device to measure and calculate the position [5]. Applicability: Basically applicability measures the physical limitations and requirements associated with the implementation and use of certain technology in terms of technical and financial issues]. The key issues affecting the applicability of mobile positioning methods are power consumption, hardware size, software size, processing load, supported positioning modes, network dependency, signal load, cost and standardization. [6]. Accuracy and Precision: Location accuracy of a geolocation method is defined to be the distance between the estimated location and the true location of the mobile station. It defines how close the location measurements are to the actual Page 1230

3 location of the mobile station being located. Accuracy is expressed in meters. A location system should report locations accurately and consistently from measurement to measurement. In evaluating the accuracy of a positioning device, one has to consider how many location measurements are made, how location measurements are scored and in what form the results are presented. There are a number of performance metrics for location accuracy in the literature. Root Mean Square Error (RMSE), Mean Squared Error (MSE), Circular Error Probability (CEP), and Cramer -Rao Lower Bound (CRLB). A widely used score function in accuracy evaluation of multiple location measurements is the square root of the mean of squared errors (RMSE), where RMSE is given by: RMSE = and d = ( )2 + ( )2 Where (X est, Y est ) is the estimated coordinates, and (X true,y true ) is the true location coordinates of the mobile station. Where d is are distance errors and n denotes the number of location estimates. RMSE is the most applicable location accuracy performance metric in the literature. It is the most used and a simple metric. Table 1.Grading of accuracy levels Accuracy Low Medium Margin of error Greater than 150m 50 to 150m High Less than 50m. Cost: We can assess the cost of a location-sensing system in several ways. Time costs include factors such as the installation process's length and the system administration needs. Space costs involve the amount of installed infrastructure and the hardware's size and form factor. Limitations: Some systems will not function in certain environments. This limitation has implications for the kind of applications we can build. Coverage: Coverage in telecommunications systems is related to the service area where, at a bare minimum, access to the wireless network is possible. For the mobile location techniques under consideration in this paper, coverage corresponds to the availability of a sufficient number of measurements to perform a location computation III. THE METHOD During the course of project, different techniques were studied that have been used in the past; in an effort to find a suitable method that would work, keeping in mind the technological and monetary constraints? One of the most renowned telecommunication companies of Pakistan was approached, in an effort to find out the details of the equipment they had used to set up the network. It was observed that most telecommunication companies currently in Pakistan have purchased only minimal equipment that is necessary to provide basic services of GSM to its users. Network providers in Pakistan were also reluctant to implement a location based system mainly due to their concerns regarding privacy of users information. This motivated the team to search for a solution that makes use of the existing network parameters without incurring any additional cost of equipment to network providers. The proper hardware based solution is very costly as discussed earlier. The proposed system is a handset based technique that uses Network Measurement Report (NMR). NMR contains cell information such as serving cell ID, signal strengths monitored by the handset and the Timing Advance (TA) parameters (figure 1). The mobile device regularly forwards the NMR to the serving cell to assist the network to make handoff decisions. If the NMR can be retrieved, then on the basis of cell id s and received signal strengths from 3 or more BTS, it is possible to triangulate users location. This can be done by approximating the user s distance from 3 or more locations on the basis of the signal strength received. [5] Page 1231

4 Figure 1: Triangulating a mobile device using Timing Advance Many researchers have tried to find a suitable relation between signal strength and distance. The main problem with signal strength is that it varies greatly according to terrain and changes drastically when a mobile device is present indoor due to multipath propagation 1. There is no universal relation that can be used to calculate signal strengths for every location on the earth. For each location, site specific relations must be formulated. Okumara-Hata models [6, 7] are the most famous models for distance calculations using signal strengths but it is valid only for Japanese terrain. Recently, signal fingerprinting has been adopted as another technique for locating a mobile device. This method implies that the received signal is extremely site-specific because of its dependence on the terrain and intervening obstacles. So the multipath structure of the channel is unique to every location and can be considered as a fingerprint or signature of the location if same RF signal is transmitted from that location. This property has been exploited in propriety systems to develop a signature database [3] of a location grid in specific service areas. The received signal is measured along this grid and recorded in the signature database. When a mobile device moves in the same area, the signal received from it is compared with the entry in the database, and thus its location is determined. Such a scheme may also be useful for indoor applications where the multipath structure in an area can be exploited. A variation of this finger printing method was used for this project. Instead of a signature database, we used a constant k as a signature and derived different values of k at different locations using a GPS/OPNET. More than 40 values of k are calculated for a desired area and an average is computed. This value is then stored in a database along with its corresponding calculated signal strength. The value of k remains constant as long as the network parameters are unchanged. A change in network parameters inevitably ensures a change in the values of k, which must then be computed again. This is the drawback of finger-printing method, it requires that the covered area be continually monitored and the signature database be continually updated. IV. TRIANGULAING A MOBILE USER Triangulating a mobile device involves three steps. The first step is to retrieve the signal strength values from the related BTS. The second step involves approximating the distances from the corresponding BTS with the help of the received signal strength. The final step is geo-coding which involves finding the actual geographic location of the mobile device. This section discusses the last part. Triangulation is defined as a process by which the location of a radio transmitter can be determined by measuring either the radial distance, or the direction, of the received signal from two or three different BTS points. Triangulation is used in cellular communications to pinpoint the geographic position of a mobile user. An example of triangulation is now given to clarify the concepts. An object A sends out a RF signal. Three base stations pick up the signal, calculate the signal strengths and use calibrated data to calculate the approximate distance based on signal strength. A server then aggregates the values to triangulate the precise position of the object A. This is one way of triangulation, the other method involves three objects sending a signal and that signal is received at a single station. To triangulate, one must know the trigonometric and the geographic formulas and their relationships so that one can perform transformation between these coordinates axis. Page 1232

5 Intersection of two circles The triangulation mechanism is an extension of the two circle intersection problem. The two circle intersection seems simple enough at first glance but in fact it is not. The major problem in two circle intersection is that both the circle can intersect at 2 points, at 1 point or do not intersect at all. A comprehensive solution is required that accounts for all the above mentioned scenarios. Let (a, b) and (c, d) be the centers of circles with radii r and s. These circles can be defined by the following equations: The three circle intersection is merely an extension of this problem and involves finding the intersection points of three circles and determining the most suitable point which is known as the epicenter. V. ANALYSIS OF THE RESULTS The general relationship between the received signal strength and distance is given below in figure 2. These equations are in 2nd degree power, and hence difficult to be solved through programming techniques. In order to solve them, these equations must first be converted to linear form. Among many solutions, one of the most convenient solutions involves translation and rotation of the axis to find the intersection points. This algorithm is discussed as follows: Figure 2- Relationship between distance and RX Levels We use the following equation [8] to calculate the value of k ; we use the above equation to determine values of k for different areas around the base stations. Generally the signal strengths around base stations follow the pattern shown in the figure 3. Page 1233

6 Figure 5: Comparison of distance with signal strength Distance variations respect to Signal Strength VI. CONCLUSION Figur Figure 3: The signal strength value pattern We found values of k which were effective for our terrain. Comparison of value of k with signal strengths and distances are given below in excel graphs of figure 4 & 5. 4: Comparison of value of k with signal strength Figure 4 clearly shows that the values of k increase exponentially with the decrease in signal strength. However, the signal strength and distance variation is not that drastic. Using these values of k, the comparison between the distances and signal strengths is given below in figure 5. We have presented an approach to approximate the location or position of a mobile user based upon signal strength of a mobile device by using different values of k. In addition we have shown that this approach provides better results in terms of accuracy as compared to signature database approach, which requires that the covered area be continuously monitored and the signature database be continuously updated. Future research includes the provision of Location Based Services (LBS) in a region after approximating the location of cellular device. REFERENCES [1] Wireless Geo location System Architecture df [2] FCC Report and Order and Further Notice of Proposed Rule Making", FCC Docket96-264, June [3] Mika Keski-Heikkilä, End-user Location in Digital Mobile Networks. S Post-Graduate Course in Radio Communications th February, 2002 [4] WJ Buchanan, J.Munoz, R.Manson,, K Raja, Analysis and Migration of Location-Finding Methods for GSM and 3G Networks. [5] Isaac K Adusei, K. Kyamakya, Klaus Jobmann. Mobile Positioning Technologies in Cellular Networks: An Evaluation of their Performance Metrics University of Hannover Institute of Communications Engineering, Hannover Germany [6] L. Lopes, E. Viller, B. Ludden, GSM standards Activity on location. IEE Colloquium on novel methods of Location and tracking of cellular mobiles and their systems Applications [7] Muhammad Aatique, Evaluation of TDOA Techniques for Position Location in CDMA Systems. Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University, September 1997 [8] Jim Costabile, Wireless Position Location. Virginia Tech Wireless Symposium, June 4, 2010 [9] Benjamin Woodacre, TDOA Positioning Page 1234

7 Algorithms: Evaluation and Implementation September 23, 2003 [10] Hakan Senturk, Performance Evaluation of Hyperbolic Position Location Technique in Cellular Wireless Networks. Thesis Presented to the Faculty of the Graduate School of Engineering and Management of the Air Force Institute of Technology, March Page 1235