Wireless Location In Cdma Cellular Radio Systems

Wireless Location in CDMA Cellular Radio Systems

With the increasing market penetration of cellular telephones, the number of E-911 calls placed by cellular telephones has grown cons- erably. This growth in E-911 calls led to a 1996 FCC ruling requiring that all cellular, PCS, and SMR licensees provide location information for the support of E-911 safety services. The provision of such location information is to be implemented in two phases. Phase I, whose deadline has already been passed, requires that wireless carriers relay the caller’s telephone number along with location of the cell site and/or sector se- ing the call, to a designated Public Safety Answering Point (PSAP). This information allows the PSAP to return the call if disconnected. Phase II, to be completed by October 1, 2001, is much more stringent and requires that the location of an E-911 caller be determined and reported with an rms location accuracy of 125 m in 67% of the cases. The applications of wireless location technology extend well beyond E-911 services. Location information can be used by cellular telephone operators themselves for more effective management of their radio - sources, so as to achieve greater spectral efficiencies. Resource m- agement algorithms such as hand-offs between cell sites, channel assi- ments, and others can all benefit from subscriber location information. Location information obtained from vehicular based cellular telephones can be used as an input to Intelligent Transportation Systems (ITS), and in particular traffic management and traveler information systems.

Wireless Location in Cdma Cellular Radio Systems

On Timing-Based Localization in Cellular Radio Networks

The possibilities for positioning in cellular networks has increased over time, pushed by increased needs for location based products and services for a variety of purposes. It all started with rough position estimates based on timing measurements and sector information available in the global system for mobile communication (gsm), and today there is an increased standardization effort to provide more position relevant measurements in cellular communication systems to improve on localization accuracy and availability. A first purpose of this thesis is to survey recent efforts in the area and their potential for localization. The rest of the thesis then investigates three particular aspects, where the focus is on timing measurements. How can these be combined in the best way in long term evolution (lte), what is the potential for the new narrow-band communication links for localization, and can the timing measurement error be more accurately modeled? The first contribution concerns a narrow-band standard in lte intended for internet of things (iot) devices. This lte standard includes a special position reference signal sent synchronized by all base stations (bs) to all iot devices. Each device can then compute several pair-wise time differences that corresponds to hyperbolic functions. Using multilateration methods the intersection of a set of such hyperbolas can be computed. An extensive performance study using a professional simulation environment with realistic user models is presented, indicating that a decent position accuracy can be achieved despite the narrow bandwidth of the channel. The second contribution is a study of how downlink measurements in lte can be combined. Time of flight (tof) to the serving bs and time difference of arrival (tdoa) to the neighboring bs are used as measurements. From a geometrical perspective, the position estimation problem involves computing the intersection of a circle and hyperbolas, all with uncertain radii. We propose a fusion framework for both snapshot estimation and filtering, and evaluate with both simulated and experimental field test data. The results indicate that the position accuracy is better than 40 meters 95% of the time. A third study in the thesis analyzes the statistical distribution of timing measurement errors in lte systems. Three different machine learning methods are applied to the experimental data to fit Gaussian mixture distributions to the observed measurement errors. Since current positioning algorithms are mostly based on Gaussian distribution models, knowledge of a good model for the measurement errors can be used to improve the accuracy and robustness of the algorithms. The obtained results indicate that a single Gaussian distribution is not adequate to model the real toa measurement errors. One possible future study is to further develop standard algorithms with these models.