Jukka Talvitie

Distance-Based Interpolation and Extrapolation Methods for RSS-Based Localization With Indoor Wireless Signals

Wireless local area network (WLAN)-based fingerprinting using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, one widely recognized problem in fingerprinting is the collection and maintenance of a proper fingerprint database. In this paper, we consider having an incomplete fingerprint database with realistic coverage gaps, and we study the performance of several interpolation and extrapolation methods for recovering the missing fingerprint data. For this purpose, we have collected an extensive set of data at frequency bands of 2.4 and 5 GHz from one university building with four floors. The accuracy of the interpolation and extrapolation methods is studied by artificially removing fingerprints from the database using a randomized procedure and by comparing the estimated fingerprints with the original fingerprints. The average RSS estimation error of different interpolation and extrapolation methods is shown for various percentages of missing fingerprints. In addition, a cumulative RSS error distribution is studied to reveal the dispersion of the error statistics, which affect the user positioning accuracy. Here, the user positioning accuracy is defined in terms of horizontal positioning error and floor detection probability. The user positioning accuracy is also compared in four cases, namely when using the original fingerprints, the partial fingerprints, the interpolated fingerprints, and the interpolated and extrapolated fingerprints. It is shown that both the horizontal positioning accuracy and the floor detection probability can be improved with proper interpolation and extrapolation methods. However, it is also illustrated that the best positioning performance is not necessarily achieved with the best average interpolation and extrapolation accuracy, but it is important to avoid certain types of errors in interpolation and extrapolation.

Radio Interface Evolution Towards 5G and Enhanced Local Area Communications

The exponential growth of mobile data in macronetworks has driven the evolution of communications systems toward spectrally efficient, energy efficient, and fast local area communications. It is a well-known fact that the best way to increase capacity in a unit area is to introduce smaller cells. Local area communications are currently mainly driven by the IEEE 802.11 WLAN family being cheap and energy efficient with a low number of users per access point. For the future high user density scenarios, following the 802.11 HEW study group, the 802.11ax project has been initiated to improve the WLAN system performance. The 3GPP LTE-advanced (LTE-A) also includes new methods for pico and femto cells interference management functionalities for small cell communications. The main problem with LTE-A is, however, that the physical layer numerology is still optimized for macrocells and not for local area communications. Furthermore, the overall complexity and the overheads of the control plane and reference symbols are too large for spectrally and energy efficient local area communications. In this paper, we provide first an overview of WLAN 802.11ac and LTE/LTE-A, discuss the pros and cons of both technology areas, and then derive a new flexible TDD-based radio interface parametrization for 5G local area communications combining the best practices of both WiFi and LTE-A technologies. We justify the system design based on local area propagation characteristics and expected traffic distributions and derive targets for future local area concepts. We concentrate on initial physical layer design and discuss how it maps to higher layer improvements. This paper shows that the new design can significantly reduce the latency of the system, and offer increased sleeping opportunities on both base station and user equipment sides leading to enhanced power savings. In addition, through careful design of the control overhead, we are able to improve the channel utilization when compared with LTE-A.

Statistical path loss parameter estimation and positioning using RSS measurements in indoor wireless networks

A Bayesian method for dynamical off-line estimation of the position and path loss model parameters of a WLAN access point is presented. Two versions of three different on-line positioning methods are tested using real data. The tests show that the methods that use the estimated path loss parameter distributions with finite precisions outperform the methods that only use point estimates for the path loss parameters. They also outperform the coverage area based positioning method and are comparable in accuracy with the fingerprinting method. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization method is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.

Deconvolution-based indoor localization with WLAN signals and unknown access point locations

In this paper, the problem of Received Signal Strength (RSS)-based WLAN positioning is newly formulated as a deconvolution problem and three deconvolution methods (namely Least Squares, Weighted Least Squares and Minimum Mean Square Error) are investigated with several RSS path loss models. The deconvolution approaches are compared with the fingerprinting approach in terms of performance and complexity. The main advantage of the deconvolution-based approaches versus the fingerprinting methods is the significant reduction in the size of the training database that need to be stored at the server side (and transferred to the mobile device) for the WLAN-based positioning. We will show that the deconvolution based estimation can decrease of the order of ten times the size of the training database, while still being able to achieve comparable root mean square errors in the distance estimation.

Access point significance measures in WLAN-based location

This paper focuses on the WLAN-based indoor location by taking into account the contribution of each hearable Access Point (AP) in the location estimation. Typically, in many indoor scenarios of interest for the future location services, such as malls, shopping centers, airports or other transit hubs, the amount of hearable APs is huge and it is important to find out whether some of these APs are redundant for the purpose of location accuracy and may be dropped. Moreover, many APs nowadays are multi-antenna APs or support multiple MAC addresses coming from exactly the same location, thus it is likely that they may bring little or no benefit if keeping all in the positioning stage. The purpose of our paper is to address various significance measures in WLAN-based location and to compare them from the point of view of the accuracy of the location solution. The access point significance is studied both at the training stage and at the estimation stage. Our models are based on real measurement data.

Statistical path loss parameter estimation and positioning using RSS measurements

An efficient Bayesian method for off-line estimation of the position and the path loss model parameters of a base station is presented. Two versions of three different on-line positioning methods are tested using real data collected from a cellular network. The tests confirm the superiority of the methods that use the estimated path loss parameter distributions compared to the conventional methods that only use point estimates for the path loss parameters. Taking the uncertainties into account is computationally demanding, but the Gauss-Newton optimization methods is shown to provide a good approximation with computational load that is reasonable for many real-time solutions.

Received signal strength models for WLAN and BLE-based indoor positioning in multi-floor buildings

This paper investigates the similarities and differences of the signal strength fluctuations and positioning accuracy in indoor scenarios for three types of wireless area networks: two Wireless Local Area Networks (WLANs) at 2.4 GHz and 5 GHz frequency, respectively, and one Wireless Personal Area Network (WPAN), namely the Bluetooth Low Energy (BLE). Two path-loss models based on weighted centroids and non-negative least squares estimation are presented: one including a floor loss factor, and the other one ignoring the floor losses, and the three signal types are compared in terms of the path-loss parameters, channel fluctuations and positioning accuracy, namely the distance errors and floor detection probabilities. The comparison is done based on real-field measurement data collected from a university building in Tampere, Finland. It is shown that all these three signal types have similar shadowing variances and close path-loss parameter values, and that a path-loss model considering floor losses gives the best floor detection probability, but not necessarily the smallest distance error.

Hybrid WLAN-RFID Indoor Localization Solution Utilizing Textile Tag

This letter presents a novel hybrid configuration for indoor positioning, utilizing the passive radio frequency identification (RFID) and wireless local area network (WLAN). Our architecture is based on a mobile device with a WLAN receiver, a textile RFID tag, and one or several RFID readers communicating with the mobile. The proposed passive textile RFID tag provides a very cost effective, power efficient, and easily implemented solution for human positioning and tracking applications. In addition, the joint utilization of two technologies increases the accuracy of the indoor positioning service. Our main contribution comes from the innovative RFID-WLAN hybrid architecture based on received signal strengths and able to improve the localization accuracy compared to pure RFID and pure WLAN location solutions. The proposed algorithm is tested with real-field measurements.

WLAN and RFID Propagation channels for hybrid indoor positioning

Indoor localization based on Received Signal Strengths (RSS) or on some form of power measurements is a low-cost and low-complexity solution gaining more and more interest in the research and commercial worlds. Typically, Wireless Local Area Network (WLAN) signals are employed for such purpose, due to the fact that they are widely spread in indoor environments. Nevertheless, any wireless signal available in indoor scenarios can be used for positioning based on similar power measurement approaches. One example is the radio frequency identification (RFID), which enable portable localization systems in the form of wearable RFID tags. Such RFID-enabled systems are highly demanded for health-state monitoring, object tracking, and security. These applications are mostly indoor applications, and thus, we can envision a near future where multiple RFID and WLAN signals will co-exist on multiple frequency bands. The existing signal diversity can offer a benefit in indoor positioning, providing that the signal propagation effects for both WLAN and RFID are well understood and taken into account. However, measurement-based studies on indoor channel modeling, including path loss and shadowing effects of RFID signals are still missing. A comparison between RFID and WLAN channel models for positioning purpose has yet to be made. It is the purpose of our paper to address the path-loss channel models and shadowing effects for WLAN and RFID signals based on extensive measurement campaigns in an office environment.

Low latency radio interface for 5G flexible TDD local area communications

This paper presents a low latency radio interface design for future 5G local area communications that provides transmission latencies less than 1 ms while providing sufficient spectral efficiency. We concentrate on the excellent latency aspects of the proposed 5GETLA radio interface and discuss the factors leading to very low latency and high energy efficiency. In addition, we study two different radio interface parameterizations and compare their total overheads and achievable transmission times.