Indoor Positioning Using Femtocells

Varun Khaitan, Peerapol Tinnakornsrisuphap and Mehmet Yavuz Qualcomm Inc.

5775 Morehouse Dr., San Diego, CA 92121 USA E-mails: {vkhaitan, peerapol, myavuz}@qualcomm.com

Abstract— The ability to locate oneself indoors on a map and

navigate to desired areas has a wide range of applications. GPS and macro cell tower based positioning are not very effective indoors due to poor signal quality or limited accuracy in location estimation. We propose to solve this problem by deploying an indoor network of femtocells and determining the position of a 3G mobile using the downlink and uplink signals. The measurement of signals from a group of femtocells at the mobile and the measurement of the mobile’s transmitted signal at group of femtocells is used for triangulation of the mobile’s position. It is shown that a combination of these methods with a cleverly designed transmission schedule involving time orthogonalization of the femtocell signals can enable accurate indoor positioning.

Keywords: femtocell, interference, indoor, position, triangulation.

I. INTRODUCTION Indoor positioning and navigation can open up a large

ecosystem of applications. To name a few, consumers/workers will be able to locate themselves and friends in public places, find the path to points of interest and receive information on services in their immediate vicinity. Satellite-based positioning systems perform poorly indoors as the signals from the satellites are too weak to be decoded. Traditional terrestrial-based positioning techniques used in macro cellular environments also may not yield satisfactory accuracy required for indoor applications.

Femtocells are low power base stations (BS) that extend the range of and offload traffic off conventional wide area network base stations or macrocells. They offer multiple benefits to subscribers and operators. To subscribers, they promise excellent user experience (better coverage and higher data throughput) and access to specialized femtozone applications. For operators, traffic will be offloaded from macro cellular network thus offsetting the need to deploy additional cell towers to sustain the increase in mobile data usage. Overview of UMTS (Universal Mobile Telecommunications Systems) femtocell (HNB) architecture and cdma2000 femtocell architecture can be found in [1].

A network of femtocells deployed in building offers a promising solution for indoor positioning as the coverage of each femtocell is small and a finer resolution may be achieved via positioning techniques such as signal strength triangulation. Furthermore, the use of femtocells can facilitate the positioning of existing 3G mobiles with no modifications nor requiring support from any additional radio technology. This can be a very important and distinguishing application for femtocells.

In this paper, we discuss key methods to identify the position of a mobile in an indoor femtocell network. We

provide detailed implementation of the methods as well as potential shortcomings and how they can be overcome. The paper is organized as follows. Section II provides an overview of different positioning techniques used in current cellular networks. Section III presents the two broad classes of techniques which can be used for positioning using femtocells. Sections IV and V describe techniques based on downlink and uplink signal strengths respectively. Section VI describes the simulation model used to evaluate the performance of these methods. Sections VII and VIII present the performance results and techniques to enhance the performance further.

II. BACKGROUND

A. Overview of Positioning Techniques Signal Strength Triangulation and Fingerprinting is a

method where the location of a mobile is estimated by obtaining a set of signal strength measurements from a group of transmitters and matching this set, known as fingerprint, against a database of measurements from a grid of points in the coverage area. The database is generated either by physically making measurements at a large number of locations or by using a computational network planning tool. In [2], such a scheme has been evaluated experimentally for an outdoor cellular network and the measurements include signal strength as well as timing advance. Similar approaches are evaluated in [3] and [4] for a commercial cellular system and an indoor environment of wireless LANs.

Advanced Forward Link Trilateration (AFLT) [5] is a positioning technology that relies on a time difference of arrival of signals from multiple base stations at the mobile. To determine location, the mobile reports the measurements of signals from nearby synchronized base stations back to the network, which are then used to triangulate an approximate location of the mobile.

Highly Detectable Pilots (HDP) is a standardized pilot for cdma2000 1xEV-DO (Evolution - Data Optimized) air-interface that is transmitted in a dedicated slot with either fixed or pseudo-random reuse pattern among neighboring cells [6]. This time multiplexed transmission allows for improved hearability of pilot signals from multiple cells.

Observed Time Difference Of Arrival (OTDOA) [7] is a standardized positioning method for UMTS where the observed time difference of pilots between a pair of base station signals at the mobile is used to calculate an estimate of the position (as a hyperboloid) and optionally, the velocity of the mobile. The mobile's position is determined by the intersection of the hyperbola for at least two pairs of base stations. OTDOA can be enhanced by performing the measurement of the non-serving cell during Idle Period

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Downlink (OTDOA-IPDL) of the serving cell, yielding similar benefits to HDP.

Uplink Time Difference Of Arrival (UTDOA) [7] is also a standardized positioning method for UMTS where the observed time difference is calculated between the mobile and a pair of Location Measurement Units (LMUs). The observed time difference is calculated by maximizing the correlation of time-shifted received signals at the LMUs. B. Femtocell Overview

A femtocell may be deployed in the same frequency channel with the macrocell (co-channel deployment) or in a separate channel that is not in use by the macrocell (dedicated channel deployment). When a mobile comes in close proximity of a femtocell, it detects the femtocell pilot/beacon and makes a handoff from the macrocell. Mobiles that are operating on the same channel with the femtocell detect the pilot through a neighbor list pilot search. For mobiles on the macro-only channels, handoff is enabled through transmission of pilot beacons [8][9]. Alternatively, the mobile may autonomously perform inter-frequency scan due to weak macrocell pilot or proximity to the femtocell [10]. Once on the femtocell, the set of pilots searched is controlled by the femtocell network.

Interference management is a critical aspect for successful deployment of femtocells. In order to provide best performance, the femtocells need to transmit their pilots and beacons with enough power to ensure satisfactory coverage while limiting the interference caused to mobiles on macrocells. In this analysis, the transmit power of femtocells on all channels is assumed to be calibrated for best performance as discussed in [12].

III. FEMTOCELL BASED POSITIONING METHODS To locate a mobile device in a network of femtocells,

whose locations are known, we need to determine its position relative to at least three femtocells to achieve successful triangulation. The distance between the mobile and a femtocell is estimated by calculating the signal propagation loss (pathloss) between them or the time taken by the signal to propagate from one point to the other.

A. Signal Strength Triangulation based methods: The presence of various obstructions in indoor

environment (furniture, walls, etc.) makes it impossible to have an accurate mathematical model describing the relation between distance and pathloss [11]. We implement a solution where we generate a database of pathloss at all locations via ray-tracing simulation of detailed building interiors using WinProp software tool. The pathloss at the user’s location can then be matched against the database to estimate the position.

B. Time based methods The signal propagation delay between femtocells and mobile, although useful in calculating distance, is ineffective as 1) The indoor radio propagation is location-dependent,

comprises of severe multipath and is unlikely to have line-of-sight between the transmitter and receiver [11].

2) The resolution of the time estimate for 3G mobiles is insufficient for indoor applications. The accuracy for OTDOA measurements during IPDL gap is ±0.5 chip [13] (which is equivalent to ±39 meters uncertainty).

Therefore, we focus on the pathloss based method to allow effective indoor positioning of existing 3G mobiles with femtocells.

IV. POSITIONING BASED ON DOWNLINK SIGNAL STRENGTH

The position of a mobile can be estimated by measuring the strength of the received downlink signals at the mobile from a group of femtocells. When a position fix is needed, either the client in the mobile triggers the measurement or the serving femtocell requests the mobile to report signal strength measurements from all the visible femtocells. If the client is in the mobile, it is assumed that the mobile has some application layer protocols to communicate the measurement and obtain the map and position fix with a server.

To perform the measurement, the mobile is assumed to be in an active call. If the user is not active, a dummy call can be initiated. The specific procedures for reporting the pilots are slightly different for cdma2000 1x and UMTS.

In cdma2000, the serving femtocell requests the mobile to send a PSMM (Pilot Strength Measurement Message), either once or periodically. As part of the PSMM report, the mobile sends the Ecp/Io of all femtocells which are measurable and the total Io, where Ecp is the received signal strength of the serving femtocell pilot and Io is the total received energy on the serving femtocell frequency (as measured by the mobile). The pathloss to each visible femtocell can then be calculated using the femtocell transmit powers. A similar procedure, Candidate Frequency Search is used to report beacon signal strengths measured on the macro frequencies.

In UMTS, the serving femtocell requests the mobile to send a Measurement Report Message (MRM) which contains Ecp/Io and Ecp information.

In either case, the fingerprint is matched against the database, containing pathloss values from all points in the network’s coverage region to all femtocells. The point with the maximum likelihood is reported as the predicted location. This procedure is described in detail in Section VI.

The positioning accuracy depends on the number of femtocells whose downlink strength can be measured. A mobile can detect the pilot from a femtocell only if the signal to interference-plus-noise ratio for the pilot (SINR or Ecp/Io) is above a detection threshold (typically around -20 dB). The downlink from other femtocells and macrocells (if the femtocell operates on a channel that is shared or adjacent to a macrocell channel) are interferers and affect the triangulation.

When a mobile is close to a femtocell, the interference generated by the serving cell to the non-serving cells is high and the triangulation set is degenerated to one. It is assumed that the communication is on a licensed spectrum and there is no spurious interference. If the triangulation set is one, the location of the serving femtocell is chosen to be the predicted location which leads to inaccuracies. For obtaining an

effective position fix in the close vicinity of a femtocell, enhancements to increase the number of visible signal sources are needed. The following methods can be used to create time orthogonalization of signals to avoid persistent interferences: (i) Inter-frequency Beacon Transmission and (ii) Coordinated Silence

A. Inter-frequency Beacon Transmission Each femtocell may transmit its beacon pilot on different

frequency channels in a time division multiplexed manner. Therefore, as measurements are made by the mobiles on the channels at multiple instances, the mobile will now be able to detect signals from different femtocells as all other interferers are removed. The pathloss to these femtocells can then be used as a fingerprint to determine the location of the mobile.

When the beacons are transmitted on the macro channel, there is additional interference due to the macrocell signals. The number of visible beacons would be maximized if the beacons can be transmitted on a clean channel (e.g., no macrocell transmission) for positioning purposes although that may not always be practical.

The beacons are assumed to be transmitted one at a time, i.e., the beacon measurements are spaced in time. This introduces additional positioning inaccuracies if the user is moving as successive measurements correspond to different locations. For best performance, non-interfering beacons should be grouped to transmit together in order to minimize the time required for triangulation. Note that only two beacon pilots other than the serving femtocell are needed for successful triangulation as the mobile already has measurement of the serving femtocell pilot on its traffic channel.

B. Co-ordinated Silence Techniques Techniques such as HDP and OTDOA-IPDL also help

create time orthogonalization of signals to avoid the problem of strong interference from the serving femtocell. As a result, the number of visible femtocell signals measurable by the mobiles will increase. Unfortunately, these features are optional in standards and are not widely supported in mobiles. Therefore, femtocells need to also support alternative solutions for mobiles that are not equipped with these features.

V. POSITIONING BASED ON UPLINK SIGNAL STRENGTH

As an alternative or complementary to the downlink methods, the position of a mobile can also be estimated by measuring the strength of the mobile uplink pilot as received at a group of femtocells. Since the transmit power of the mobile is unknown and dynamic, the pathloss cannot be estimated from this measurement. However, the difference of the measured strength at two femtocells is equal to the pathloss difference from the mobile’s location to these femtocells. Thus the difference in the pathloss values can be used as a fingerprint. However, this reduces the triangulation set size by one. For example, if four femtocells are visible, then the fingerprint has three values only.

The proposed method is as follows: when a mobile position is needed, the positioning server instructs all femtocells around the serving femtocell to make uplink measurements for the pilot of the mobile. Those femtocells which can sense the mobile send the measured Ecp/Nt (ratio of the mobile’s pilot strength to the sum of all other signals on the channel) and Nt values to the positioning server. The server calculates the pathloss difference to a number of pairs of femtocells and matches this against the database to predict the mobile’s position. Again, the accuracy of this method will be limited by the number of femtocells which can sense the mobile and that can be calculated analytically as follows.

As the mobile in active call will be power controlled by the serving femtocell, its uplink signal cannot be measured at far located femtocells. It can be shown that, assuming perfect power control, a non serving cell can detect the mobile only if its pathloss to the mobile satisfies the relation

non‐serving serving th th

where th is the target mobile SNR and th is the

minimum SNR below which a femtocell cannot sense the mobile signal. If the this set to -18 dB and th is -30 dB

(both are typical values), only those femtocells whose pathloss to the mobile is within 12dB of the pathloss to the serving femtocell can detect the mobile. This results in a small triangulation set and thus poor positioning accuracy. The performance can be improved by increasing the SNR target for the mobile from -18 dB to -10 dB and which increases the pathloss margin from 12 dB to 20 dB. The increase in the target Ecp/Nt has to be carefully evaluated as it also increases interference to the uplink of macrocells in co-channel deployments. However, since the pathloss from a mobile to serving femtocell is generally very small compared to typical pathloss to the macrocell, the increase may be acceptable in typical deployments. Further, the target Ecp/Nt can be increased selectively on the mobile that is being positioned.

VI. SIMULATION MODEL We have developed a detailed pathloss model of an

enterprise building using the WinProp software tool. Using field measurements we have been able to verify the accuracy of the modeling software to be within 3-5 dB of the actual values.

A. Building model An enterprise building, Qualcomm Research Center, has been modeled in a detailed manner using building floor plans which can be directly imported to the software. Care has been taken while choosing the construction material, both internally and externally, in order to accurately model the pathloss from the femtocells to different areas of the building. The modeling has been done on a very fine grid of size 0.2 meters.

B. Simulation Assumptions We assume a dedicated frequency deployment where the

femtocells are on a clean channel. There are multiple macro

channels, one being adjacent to the femtocell channel, and beacons are transmitted on all of them. For our analysis, the macrocells and femtocells are assumed to be 50% loaded, (ie) their power is 50% of their allowed maximum. It is also assumed that 20% of the full power is allocated to the pilot for both macrocells and femtocells.

Channel fading is simulated and added to the path loss. A Rician fading model with K-factor = 1.5 is used to model fading on the femtocell downlink and K-factor = 5.0 is used to model fading on the macrocell downlink. Doppler effect is modeled based on the user speed and traffic.

VII. PERFORMANCE RESULTS The performances of the methods discussed earlier are

presented in this section along with their shortcomings and suggested improvements.

A. Femtocell Downlink Measurement Method To simulate the performance of this method, we generate the

database of Ecp/Io strength seen at all points on the grid from all the femtocells on the floor. Please note that six femtocells are required for complete coverage area (42000 sq ft) as the recommended coverage area per femtocell is 6000-8000 sq ft for indoor office environments [12]. For each position of the user on the grid, the estimated position is obtained using the steps in Figure 1. In this process, visible femtocells refer to the femtocells whose Ecp/Io is above the detection threshold which is assumed to be -20 dB.

Our field tests show that, due to fading and multipath, the pilot strength of a femtocell at a particular point can vary approximately uniformly over time in a range of 6 dB. Thus in Step 4 the likelihood function is based on a uniform distribution on the interval [-3, 3]. For example, if the mobile reported pathloss of -90 dB to a femtocell, the mobile would be equally likely to be on any point in the database that has a pathloss value in [-93, -87] dB to the femtocell. The predicted point in Step 5 is chosen to be the mean value of all the equally likely points so as to minimize the expected root-mean-square (RMS) value of the error.

Figure 1 Calculation of position using downlink signal triangulation

The RMS error in estimated position at each point on the

floor calculated as an average over 30 faded instances is shown in Figure 2.

Figure 2 RMS error of predicted position using downlink

triangulation

The results show that the method is very accurate in regions of overlapping coverage between multiple femtocells as more femtocells are visible. In the regions close to a femtocell, only one femtocell is in the visible set as it creates high interference to all other femtocells. As the predicted point is the location of the femtocell itself, this also explains how the error is low at the femtocell location and increases as the actual mobile location moves outward.

The efficacy of this method is compromised by the hearability problem. This is illustrated in Figure 3 which shows the number of visible femtocells at each location for a dedicated femtocell deployment, the enterprise being located close to a macrocell.

Figure 3 The number of visible femtocells at each location

As discussed in Section IV, the hearability problem can be alleviated via time orthogonalization techniques. Figure 4 shows the number of pilots visible at different points for a macrocell edge deployment (enterprise is at the edge of the macrocell coverage) after orthogonalization technique is applied, assuming a minimum detection threshold of -20 dB Ecp/Io. The corresponding result for macrocell site indicates good visibility on the building side close to the macrocell and lower numbers on the other side.

Figure 4 Number of pilots visible at macrocell edge after

orthogonalization

B. Femtocell Uplink Measurement Method The performance of the uplink method is simulated using a slightly modified procedure from the downlink. As stated earlier, the pathloss from a femtocell varies uniformly in a 6 dB interval around the actual value in field measurement. Thus the difference in pathloss values from two femtocells follows a triangular distribution, i.e., the convolution of two uniform distributions, between +6dB and -6dB.

Upon receiving the measurements from femtocells where the user can be sensed, the set of pathloss differences is computed. At each point in the database, the likelihood of it being the mobile’s current location is calculated and point which maximizes the likelihood function is chosen as the predicted position. The accuracy of the method depends on the number of femtocells that can measure the mobile pilot at different points. The results in Figure 5 are generated using target Ecp/Nt setpoint of -10 dB as discussed in Section V. The performance with a setpoint of -18 dB is quite unsatisfactory.

Figure 5 Visible set size for uplink triangulation

Overall the performance of the uplink method is worse than downlink as the triangulation set is always smaller by one. From Figure 3 and Figure 5, we see that when the mobile is very close to the serving femtocell, both the downlink and uplink measurement methods fail to triangulate the position.

VIII. CONCLUSIONS In this paper we presented methods using femtocells to

provide accurate indoor positioning of mobiles. It was shown that downlink signal strength based methods work well when the mobile is not close to a femtocell. When near a femtocell,

the performance can be improved by time orthogonalization techniques. On the uplink, the triangulation performance can be improved by selectively raising the target setpoint of the mobile. The proposed methods are to be tested in a real world system to understand the accuracy in presence of actual fading and measurement errors.

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