Tems Aspects of Geo Location in Mobile Networks

Prepared by: Date: Document: Irina Cotanis 17 November 2010 Rev 17 Nov 2010 Ascom (2010) All rights reserved. TEMS is a trademark of Ascom. All other trademarks are the property of their respective holders.

Aspects of Geo-Location in Mobile Networks

White Paper

Ascom (2010) Document: Rev 17 Nov 2010 2(11)

Contents

1 Introduction ................................................................ 3

2 UE localization beyond networks LCS function ....................................................................... 3

2.1 3GPP LCS specifications ......................................................3

2.2 Accuracy aspects in 3GPP LCS ...............................................4

2.3 Beyond LCS scope and context ..............................................5

3 Generalities on geo-location techniques in mobile networks ........................................................ 6

4 Using OTODA geo-location technique in WCDMA ....................................................................... 9

4.1 Geo-location in WCDMA ...........................................................9

4.2 OTODA technique .....................................................................9

4.3 Accuracy related aspects ....................................................... 10

5 Conclusions ............................................................. 10

6 References................................................................ 11


1 Introduction The UE geo-location became lately an important topic for the operators who need to provide a large number of services at low OPEX and CAPEX costs. An important leverage for achieving this goal is to be able to easily access geo-location information of a particular service as well as to perform area focused and driven trouble shooting and optimization.

The paper presents some general aspects on localization services (LCS) as well as 3GPP work in this area. In addition, the paper shows the need and the means of going beyond the LCS as defined by 3GPP. Geo-location techniques that could be used, the required parameters as well as the limitations involved by any of the presented methods are discussed. The paper also describes in more detail the multilateration technique which is suitable for geo-location estimate solutions implemented in network monitoring tools for WCDMA networks. These solutions are more likely to be used for the geo-location of network problems as well as optimization.

2 UE localization beyond networks LCS function

The myriad of services that have become available to subscribers with the deployment of 3G networks has increased the need for LCS. In the last few years, 3GPP standardization efforts have uncovered a series of aspects related to LCS, from stage 1 (service description [1] (Release 9)) to stage 2 (functional description [2], [3]), and just recently, the updated functional specifications required for user equipment (UE) [4] (Release 10).

2.1 3GPP LCS specifications

3GPP specifies that the LCS function is to be based on measuring radio signals to determine the geographic position and velocity of the UE and that the position information may be requested by and reported to a client (application) associated with the UE, or by a client within or attached to the Core Network (CN).

The Radio Network Controller (RNC) needs to manage the overall coordination and scheduling of resources required to perform positioning of a UE; which may even include calculating the final position, velocity estimate, and accuracy. The UE positioning function provisioning could be performed through support of specified localization technique(s) in Node B, the RNC, and the Satellite Augmentation System (SAS) if this is available to the network [4]. In the latter case, network-based localization is possible; otherwise, UE-based localization can be accomplished.

The localization calculation is performed by a Localization Measurement Unit (LMU) or, for increased accuracy, by a combination of several LMUs. The LMU could be a stand-alone unit or it could be integrated in a Node B. Regardless of the LMU location, communication protocols must be established between the LMUs and all the other elements involved (UE, Radio Network Controller (RNC), and/or SAS) on different interfaces such as Uu, Iub, and Iur. Depending on the selected positioning technique, the


UE performs specific measurements that are used by the LMU for estimating the UE location. The UE could extend its support to the LMU if it is designed with LCS capabilities.

The reporting standard format (geographical coordinates) is specified to include the estimated errors (uncertainty) of the position and velocity of the UE and, if available, the positioning method (or list of methods) used to obtain the position estimate [4]. It should be noted that the uncertainty of the position measurement is dependent on the network implementation and might vary between networks, as well as between one area within a network and another. The uncertainty may be hundreds of meters in some areas and only a few meters in others [4].

2.2 Accuracy aspects in 3GPP LCS

General impacting factors

The accuracy that can be provided with various positioning technologies depends on a number of factors, many of which are dynamic in nature and will thus vary with the radio environment (signal attenuation and multipath propagation), network topography (density of Node Bs), geography, and available positioning equipment.

Due to this variability, accuracy could be expressed in ranges of values along with their probabilities (or certainty levels).

Horizontal accuracy

Different services require different levels of positioning accuracy, and the range may vary from tens of meters (navigation services) to kilometers (fleet management). However, the majority of attractive value-added location services are enabled when location accuracies of between 25m and 200m can be provided.

Accuracy requirements for some examples of LCS are provided in Table 1 [1]. However, it should be noted that accuracy requirements for LCS are also dependent on regional regulatory bodies [1].

Table 1. Accuracy requirements for different LCS

LCS application Accuracy requirements

Weather reports, news, traffic information regional / district

Up to 200km / up to 20km

Vehicle asset management, targeted congestion avoidance advice

Up to 1km

Rural and suburban emergency services 500m to 1km

U.S. FCC mandate for wireless emergency calls using network-based positioning methods

100m (67%); 300m (95%)

Urban SOS, localized advertising, home zone pricing, network maintenance, network demand monitoring, asset tracking

75m to 125m


U.S. FCC mandate (99-245) for wireless emergency calls using handset-based positioning methods

50m (67%), 150m (95%)

Vertical accuracy

Vertical accuracy is also specified for LCS [1]. The vertical accuracy may range from about ten meters (which would provide resolution within one floor of a building) to hundreds of meters.

Accuracy representation forms

It is expected that several estimates for a fixed UE position can be achieved; therefore, the distribution of locations can be described by normal statistical parameters, and it is suggested that a small proportion of location estimates may lie outside of the acceptable Quality of Service (QoS) parameters for specific services (as determined by the network operator). In addition, it is recommended that information on the confidence level that can be associated with a location estimate be obtained.

Accuracy negotiation

Accuracy is application driven and is one of the negotiable Quality of Service (QoS) parameters. The precision of the location should be network design dependent (i.e., precision should be the operators choice). This precision requirement may vary from one part of a network to another.

Whether horizontal or vertical, the required accuracy should satisfy (or approach as closely as possible) the requested or negotiated accuracy when other QoS parameters are not in conflict.

2.3 Beyond LCS scope and context

The 3GPP specifications related to LCS function are expected to add value to operators not only for service localization, emergency call service, or home location billing, but also for services that go beyond the scope of LCS, particularly UTRAN internal utilization such as location-assisted handover. In addition, network operators need UE positioning for network optimization and troubleshooting such as dropped and blocked calls/sessions and detection of coverage holes.

However, implementation of the 3GPP specifications involves a high level of complexity that has already pushed the LCS feature to Release 10, while still leaving it quite open and loose on aspects such as selected architecture (network-based, UEbased, and/or UE assisted), positioning technique selection, and accuracy. Therefore, the implementation decision and choices are very much dependent on infrastructure vendors, as well as phone vendors, who need to develop UE with LCS capabilities.

In addition, as was observed in paragraph 1.1., the LCS function involves extensive resource usage and definition of reporting protocols between existing network elements (e.g., UE, Node B, RNC) and/or additional elements (e.g., LMU, SAS), as well as a flexible UE positioning architecture and functions that would allow accommodation of several techniques of


measurement and processing to ensure that evolution follows changing service requirements and takes advantage of advancing technology.

Therefore, for troubleshooting and optimization, network operators could use more straightforward approaches to UE geo-location, particularly solutions implemented in testing tools such as the TEMS Visualization network event based tool. As an network event based tool, TEMS Visualization can only use parameters reported by the UE (such as Ec/No, cell ID, propagation delay, and Tm value representing Node B reception time related to UE time reference) or parameters reported by the network during different events (such as RRC_Measurement_Reports at soft handovers or during periodic measurements, and call setup). Details are on this solution, it performance and uses cases are provided in [5].

As with the 3GPP specification [1], the accuracy of the UE localization estimation performed by network testing tools is highly dependent on the technique used, as well as on the reported UE measurements on which the technique relies. The UE measurements could be impacted by the position of the UE within the coverage area and the activity of the UE. However, as with LCS cases, different use cases for the UE geo-location (e.g., coverage holes detection, dropped events positioning) require different accuracy ranges and/or distributions. Some use cases are presented and discussed in paragraph 6.

3 Generalities on geo-location techniques and parameters in mobile networks

A geo-location technique represents a principle and/or algorithm on which the estimation of geographical position is based. In the mobile network case [5], these techniques can be classified in three main categories. Adding new parameters or tuning any of these main categories, as well as combining them, results in derivates and accuracy improvements.

1. Triangulation (or AOA, angle of arrival) when AOA between the transmitter base station) and receiver (UE) and the direction of arrival is known. The AOA is generally determined by using multiple antennas at the base station (array signal processing). The geo-location estimate is generally determined based on sine theorem (or law) [12] and maximum likelihood estimation.

2. Trilateration (or TOA, time of arrival), when the absolute time of arrival from the transmitter (base station/UE) to the receiver (UE/base station) is known (e.g., TA in GSM, RTT in WCDMA). In this case, the solution of the UE geo-location is given by the population of points representing the intersection of circles described by different times of arrival. The accuracy of the positioning depends on the number of circles that are available, particularly the number of transmitters.

3. Multilateration (or TDOA, time difference of arrival) when only the time difference of arrivals from two different transmitters is known


(e.g., TDOA in WCDMA). In this case, the solution of the UE geo-location is given by the population of points representing the intersection of pairs of hyperbola. See paragraph 3 for more information.

4. Cell ID based, which estimates the UE position based on the knowledge of serving Node Bs. Accuracy of the estimation depends on the coverage area of the cells and improvements (such as adding Round Trip Time (RTT) or Received Signal Code Power (RSCP) parameters for WCDMA, or Time Advance (TA) and Received Signal Strength (RSSI) for GSM) needed in order to significantly decrease the estimation error, which might be unacceptable depending on the size of the cell area.

The main derivates of the three main categories are:

1. GPS-based techniques such as: GPS-based TOA and E-OTD (in GSM) and GPS-based TDOA (in WCDMA). As expected, adding GPS information to the above-mentioned categories increases the accuracy.

2. Prediction/propagation-based techniques when propagation profiles and/or databases are available (such as propagation database correlation) or are pre-built based on extensive measurements or pre-measurements (such as the measurement pilot correlation method, which uses pre-measured samples of RSCP of visible pilots). These techniques could increase accuracy significantly, at the price of being quite costly and complex to implement. Therefore, they are not mentioned in the 3GPP specifications.

The geo-location techniques used in mobile networks are based on the following parameters (and/or combinations of the following parameters):

1. RSCP (in WCDMA). This is the received power on one scrambling code measured on the Common Pilot Channel (CPICH). It is measured by the UE in both idle and active modes.

2. RSSI (in GSM, WCDMA). The received wideband power, including thermal noise and noise generated in the receiver. Measured by the UE in active mode only, it represents the downlink interference level at the UE side.

3. SFN-SFN. The observed time difference (in WCDMA) representing the time difference of System Frame Numbers (SFNs) between two cells:

TCPICHRxj TCPICHRxi

where TCPICHRxj is the time when the UE receives one Primary CPICH slot from Cell j and TCPICHRxi is the time when the UE receives the Primary CPICH slot from Cell I, which is the cell closest in time to TCPICHRxj.

This is measured by the UE in both idle and active modes.

4. RTT (in WCDMA), TA (in GSM):


RTT = TRX TTX

where TTX is the time of transmission of the beginning of a downlink Dedicated Physical Channel (DPCH) frame to a UE, and TRX is the time of reception of the beginning (the first detected path, in time) of the corresponding uplink Dedicated Physical Control Channel (DPCCH) frame from the UE.

The measurements are taken in active mode only and they are possible on a downlink DPCH transmitted from Node B and an uplink Dedicated Physical Data Channel (DPDCH) received in the same Node B.

5. Angle of arrival (AoA) of the signals from the mobile station at several Radio Base Stations (RBS) or Node Bs. The positioning accuracy could be poor, especially if no other information is available.

The various technologies and different parameters used as input to the geo-location methods as well as the tuning elements are expected to show different accuracy values. Table 2 provides as guidance some rough estimates of the accuracy range for different geo-location technologies used in mobile networks [6]-[8]. This guidance is providing indication on the accuracy limitations of each of technologies. For example, it should be not expected an average (or median) accuracy of about 20m if the geo-location technology used is Cell ID, or of 30m for the TDOA technology. On the other hand, it is well-known that the geo-location technologies are strongly dependent on the conditions of the measurement scenario and area. Therefore, the guidance should be interpreted that these rough estimates might be either over or under performed in some test scenarios. For example, for the Cell ID+TA technology, the geo-location accuracy could reach 100m [9], if the UE is very close to the cell sites location. Similarly, geo-location based on TDOA technique might exhibit a median error up to 600m in a suburban area characterized by larger cells and therefore less data available for the geo-location estimates calculation [5]. However, these cases might occur less frequently and therefore they do not necessarily represent the main trend of the majority of geo-location estimates.

Table 2. Rough estimates of geo-location accuracy ranges

Technology Rough errors estimate ranges Cell ID Depends on the cell size

Cell ID +TA 300 to 500m

TDOA 100 to 300m

AOA 200 to 400m

EOTD (TDOA with AGPS) 20 to 200m

GPS/AGPS 5 to 30m


4 Using OTODA geo-location technique in WCDMA

4.1 Geo-location in WCDMA

As already mentioned, for troubleshooting and optimization, network operators could use as geo-location solution testing and monitoring tools such as the TEMS Visualization network event based tool [5]. However, the main limitation is the fact that used geo-location technique is strictly dependent on the data made available from the network. The current WCDMA release provides data such as Ec/Io of observed cells, cell ID, propagation delay, and Tm value representing Node B reception time related to UE time reference) reported during different events (such as call set-up, RRC_Measurement_Reports at soft handovers or during periodic measurements, and call setup).

Therefore, the geo-location technique for WCDMA case in a network testing and monitoring tool needs to use multilateration based on TDOA values.

4.2 OTODA technique

The multilateration technique estimates the position of an object based on the Time Difference Of Arrival (TDOA) of a signal emitted from the object to three or more receivers. It also refers to the case of locating a receiver by measuring the TDOA of a signal transmitted from three or more synchronized transmitters [6]. The latter case represents the case of UE positioning based on OTDOA (Observed TDOA) in WCDMA networks. The term Observed is used because TDOA actually represents the perceived time difference between signals coming from two Node Bs to the UE.

The primary standard OTDOA measurement is the "SFN-SFN observed time difference," which is observed at the UE [4]. These measurements, together with the involved Node B synchronization information, are used to calculate an estimate of the position of the UE. Each OTDOA measurement for a pair of downlink transmissions describes a curve of constant difference along which the UE may be located. The curve of constant difference is represented in space by a hyperbola. The foci of the hyperbola are defined by the positions of the two Node Bs for which the OTDOA is determined. When multiple Node Bs are used, multiple hyperbolas are formed, and the intersection of the set of hyperbolas provides the estimated location of the UE. Therefore, the technique requires at least three Node Bs in order to estimate deterministic UE position, as shown in Figure 1.

As also can be seen in Figure 1, the calculation of the intersection of two hyperbolas (three Node Bs available) could generate two possible solutions for the estimated UE location. The optimal solution is selected after running optimization techniques, as depicted. In a scenario in which more Node Bs are available, the UE position can be determined with higher accuracy due to the intersection of more hyperbolas.


Only in an ideal case would the intersection be a single point. In reality, due to various errors, the solution will be a cloud of points, in which case advanced optimization techniques would be required. In addition, after a coarse optimization is applied, a least squares method or an extended Kalman filter could be used for finer optimization and, therefore, a more accurate estimate of the UE position.

Figure 1. Calculation of the intersection of two hyperbolas (three Node Bs available)

3GPP specifies [4] two operational modes for the OTDOA technique: UE-assisted OTDOA and UE-based OTDOA. The two modes differ in where the actual position calculation is carried out.

In the UE-assisted mode, the UE measures the difference in time of arrival of several cells and signals the measurement results to the network, where the RNC or the SAS carries out the position calculation.

In the UE-based mode, the UE makes the measurements and also carries out the position calculation, and thus requires additional information (such as the position of the measured Node Bs) for the position calculation.

4.3 Accuracy related aspects

The accuracy of the position estimates performed with the OTDOA technique depends on the following main factors:

1. The relative space distribution of the involved Node Bs. The geometry of the space distribution of the Node B positions may affect the accuracy of the UE geo-location estimate; the best results would be achieved if the Node Bs equally surrounded the UE. If they do not, accuracy is reduced, which is sometimes termed the Geometric Dilution of Position (GDP). This is caused by the fact that the hyperbola intersection may occur in the asymptotic region of the curve; therefore, an infinite number of points verify the intersections equations.

2. The relative distance between the involved Node Bs. Overshooting cells are prone to causing hyperbola intersections in the asymptotic region of the hyperbola; thereby increasing the GDP effect.


3. The precision of Node Bs reference clocks relative to each other, including time drift.

4. The effects of multipath radio propagation that could impact the UE measurements of precise times of arrival of signals received from the observed Node Bs.

In order to encompass all these factors, a large number of measurements are required for the synchronization as well as for the OTDOA calculations. The synchronization should consider a dynamic tuning to compensate for the time drift. In addition, a validity test could be run on the geometric location of the Node Bs considered in the UE position estimation.

5 Conclusions The myriad of services that have become available to subscribers with the deployment of 3G networks has increased the need for localization of services (LCS). The paper presents challenges of the implementation of the LCS feature in the network as well as recommended accuracy values. The need for a straightforward approach, especially in the use cases such as network troubleshooting and optimization is discussed. Different geo-location technologies and parameters, as well as their limitations and accuracy ranges are explained. The paper focuses then on the OTDOA technology, as the main geo-location solution in network testing and monitoring tools.

6 References [1] 3GPP TS 22.071: "Location Services (LCS); Service description,

Stage 1."

[2] 3GPP TS 23.271: "Location Services (LCS) Functional description, Stage 2."

[3] 3GPP TS 23.171: "Functional stage 2 description of location services in UMTS."

[4] 3GPP TS 25.305 V10.0.0 (2010-09): Stage 2 functional specification of User Equipment (UE) positioning in UTRAN.

[5]. I. Cotanis, Geo-location in WCDMA with TEMS Visualization Solution, white paper, Ascom Network Testing, November 2010

[6]. Shay Horovitz, Localization based services for mobile devices, Embedded Computing Seminar

[7]. Mohammad S. Sharawi et el., Investigation into the performance of EOTD for GSM users in Telematics applications, Proceedings of SPIE, vol. 5084, Aug. 2003,

[8]. Shu Wang, et el, LBS for Mobile Technologies and Standards, WCNC 2008

[9]. N. Stobie, Le Le, TEMS Visualization 4.1 GSM Estimated Positioning Accuracy , TEMS Report, October 2007

Tems Aspects of Geo Location in Mobile Networks

Documents

Transcript of Tems Aspects of Geo Location in Mobile Networks