Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Kapeankaistan LTE koneiden välisessä satelliittitietoliikenteessä

Recent trends to wireless Machine-to-Machine (M2M) communication and Internet of Things (IoT) has created a new demand for more efficient low-throughput wireless data connections. Beside the traditional wireless standards, focused on high bandwidth data transfer, has emerged a new generation of Low Power Wide Area Networks (LPWAN) which targets for less power demanding low-throughput devices requiring inexpensive data connections. Recently released NB-IoT (Narrowband IoT) specification extends the existing 4G/LTE standard allowing more easily accessible LPWAN cellular connectivity for IoT devices. Narrower bandwidth and lower data rates combined to a simplified air interface make it less resource demanding still benefiting from the widely spread LTE technologies and infrastructure. %% Applications & Why space Applications, such as wide scale sensor or asset tracking networks, can benefit from a global scale network coverage and easily available low-cost user equipment which could be made possible by new narrowband IoT satellite networks. In this thesis, the NB-IoT specification and its applicability for satellite communication is discussed. Primarily, LTE and NB-IoT standards are designed only for terrestrial and their utilization in Earth-to-space communication raises new challenges, such as timing and frequency synchronization requirements when utilizing Orthogonal Frequency Signal Multiplexing (OFDM) techniques. Many of these challenges can be overcome by specification adaptations and other existing techniques making minimal changes to the standard and allowing extension of the terrestrial cellular networks to global satellite access.Viimeaikaiset kehitystrendit koneiden välisessä kommunikaatiossa (Machine to Machine Communication, M2M) ja esineiden Internet (Internet of Things, IoT) -sovelluksissa ovat luoneet perinteisteisten nopean tiedonsiirron langattomien standardien ohelle uuden sukupolven LPWAN (Low Power Wide Area Networks) -tekniikoita, jotka ovat tarkoitettu pienitehoisille tiedonsiirtoa tarvitseville sovelluksille. Viimeaikoina yleistynyt NB-IoT standardi laajentaa 4G/LTE standardia mahdollistaen entistä matalamman virrankulutuksen matkapuhelinyhteydet IoT laitteissa. Kapeampi lähetyskaista ja hitaampi tiedonsiirtonopeus yhdistettynä yksinkertaisempaan ilmarajapintaan mahdollistaa pienemmän resurssivaatimukset saman aikaan hyötyen laajalti levinneistä LTE teknologioista ja olemassa olevasta infrastruktuurista. Useissa sovelluskohteissa, kuten suurissa sensoriverkoissa, voitaisiin hyötyä merkittävästi globaalista kattavuudesta yhdistettynä edullisiin helposti saataviin päätelaitteisiin. Tässä työssä käsitellään NB-IoT standardia ja sen soveltuvuutta satellittitietoliikenteeseen. LTE ja NB-IoT ovat kehitty maanpääliseen tietoliikenteeseen ja niiden hyödyntäminen avaruuden ja maan välisessä kommunikaatiossa aiheuttaa uusia haasteita esimerkiksi aika- ja taajuussynkronisaatiossa ja OFDM (Orthogonal Frequency Signal Multiplexing) -tekniikan hyödyntämisessä. Nämä haasteet voidaan ratkaista soveltamalla spesifikaatiota sekä muilla jo olemassa olevilla tekniikoilla tehden mahdollisimman vähän muutoksia alkuperäiseen standardiin, ja täten sallien maanpäälisten IoT verkkojen laajenemisen avaruuteen

Improvement of mobile trilateration accuracy with modified geo-location techniques.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in pdf

Investigations of 5G localization with positioning reference signals

TDOA is an user-assisted or network-assisted technique, in which the user equipment calculates the time of arrival of precise positioning reference signals conveyed by mobile base stations and provides information about the measured time of arrival estimates in the direction of the position server. Using multilateration grounded on the TDOA measurements of the PRS received from at least three base stations and known location of these base stations, the location server determines the position of the user equipment. Different types of factors are responsible for the positioning accuracy in TDOA method, such as the sample rate, the bandwidth, network deployment, the properties of PRS, signal propagation condition, etc. About 50 meters positioning is good for the 4G/LTE users, whereas 5G requires an accuracy less than a meter for outdoor and indoor users. Noteworthy improvements in positioning accuracy can be achievable with the help of redesigning the PRS in 5G technology. The accuracy for the localization has been studied for different sampling rates along with different algorithms. High accuracy TDOA with 5G positioning reference signal (PRS) for sample rate and bandwidth hasn’t been taken into consideration yet. The key goal of the thesis is to compare and assess the impact of different sampling rates and different bandwidths of PRS on the 5G positioning accuracy. By performing analysis with variable bandwidths of PRS in resource blocks and comparing all the analyses with different bandwidths of PRS in resource blocks, it is undeniable that there is a meaningful decrease in the RMSE and significant growth in the SNR. The higher bandwidth of PRS in resource blocks brings higher SNR while the RMSE of positioning errors also decreases with higher bandwidth. Also, the number of PRS in resource blocks provides lower SNR with higher RMSE values. The analysis with different bandwidths of PRS in resource blocks reveals keeping the RMSE value lower than a meter each time with different statistics is a positivity of the research. The positioning accuracy also analyzed with different sample sizes. With an increased sample size, a decrease in the root mean square error and a crucial increase in the SNR was observed. From this thesis investigation, it is inevitable to accomplish that two different analyses (sample size and bandwidth) done in a different way with the targeted output. A bandwidth of 38.4 MHz and sample size N = 700 required to achieve below 1m accuracy with SNR of 47.04 dB

ROLAX: LOCATION DETERMINATION TECHNIQUES IN 4G NETWORKS

In this dissertation, ROLAX location determination system in 4G networks is presented. ROLAX provides two primary solutions for the location determination in the 4G networks. First, it provides techniques to detect the error-prone wireless conditions in geometric approaches of Time of Arrival (ToA) and Time Difference of Arrival (TDoA). ROLAX provides techniques for a Mobile Station (MS) to determine the Dominant Line-of-Sight Path (DLP) condition given the measurements of the downlink signals from the Base Station (BS). Second, robust RF fingerprinting techniques for the 4G networks are designed. The causes for the signal measurement variation are identified, and the system is designed taking those into account, leading to a significant improvement in accuracy. ROLAX is organized in two phases: offline and online phases. During the offline phase, the radiomap is constructed by wardriving. In order to provide the portability of the techniques, standard radio measurements such as Received Signal Strength Indication (RSSI) and Carrier to Interference Noise Ratio(CINR) are used in constructing the radiomap. During the online phase, a MS performs the DLP condition test for each BS it can observe. If the number of the BSs under DLP is small, the MS attempts to determine its location by using the RF fingerprinting. In ROLAX, the DLP condition is determined from the RSSI, CINR, and RTD (Round Trip Delay) measurements. Features generated from the RSSI difference between two antennas of the MS were also used. The features, including the variance, the level crossing rate, the correlation between the RSSI and RTD, and Kullback-Leibler Divergence, were successfully used in detecting the DLP condition. We note that, compared to using a single feature, appropriately combined multiple features lead to a very accurate DLP condition detection. A number of pattern matching techniques are evaluated for the purpose of the DLP condition detection. Artificial neural networks, instance-based learning, and Rotation Forest are particularly used in the DLP detection. When the Rotation Forest is used, a detection accuracy of 94.8\% was achieved in the live 4G networks. It has been noted that features designed in the DLP detection can be useful in the RF fingerprinting. In ROLAX, in addition to the DLP detection features, mean of RSSI and mean of CINR are used to create unique RF fingerprints. ROLAX RF fingerprinting techniques include: (1) a number of gridding techniques, including overlapped gridding; (2) an automatic radiomap generation technique by the Delaunay triangulation-based interpolation; (3) the filtering of measurements based upon the power-capture relationship between BSs; and (4) algorithms dealing with the missing data. In this work, software was developed using the interfaces provided by Beceem/Broadcom chip-set based software. Signals were collected from both the home network (MAXWell 4G network) and the foreign network (Clear 4G network). By combining the techniques in ROLAX, a distance error in the order of 4 meters was achieved in the live 4G networks

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201