AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Radio channel characterisation and system-level modelling for ultra wideband body-centric wireless communications

PhDThe next generation of wireless communication is evolving towards user-centric networks, where constant and reliable connectivity and services are essential. Bodycentric wireless network (BCWN) is the most exciting and emerging 4G technology for short (1-5 m) and very short (below 1 m) range communication systems. It has got numerous applications including healthcare, entertainment, surveillance, emergency, sports and military. The major difference between the BCWN and conventional wireless systems is the radio channel over which the communication takes place. The human body is a hostile medium from the radio propagation perspective and it is therefore important to understand and characterise the effect of the human body on the antenna elements, the radio propagation channel parameters and hence the system performance. In addition, fading is another concern that affects the reliability and quality of the wireless link, which needs to be taken into account for a low cost and reliable wireless communication system for body-centric networks. The complex nature of the BCWN requires operating wireless devices to provide low power requirements, less complexity, low cost and compactness in size. Apart from these characteristics, scalable data rates and robust performance in most fading conditions and jamming environment, even at low signal to noise ratio (SNR) is needed. Ultra-wideband (UWB) technology is one of the most promising candidate for BCWN as it tends to fulfill most of these requirements. The thesis focuses on the characterisation of ultra wideband body-centric radio propagation channel using single and multiple antenna techniques. Apart from channel characterisation, system level modelling of potential UWB radio transceivers for body-centric wireless network is also proposed. Channel models with respect to large scale and delay analysis are derived from measured parameters. Results and analyses highlight the consequences of static and dynamic environments in addition to the antenna positions on the performance of body-centric wireless communication channels. Extensive measurement i campaigns are performed to analyse the significance of antenna diversity to combat the channel fading in body-centric wireless networks. Various diversity combining techniques are considered in this process. Measurement data are also used to predict the performance of potential UWB systems in the body-centric wireless networks. The study supports the significance of single and multiple antenna channel characterisation and modelling in producing suitable wireless systems for ultra low power body-centric wireless networks.University of Engineering and Technology Lahore Pakista

Design a New Tomlinson-Harashima Non-Linear Pre-Coding Technique for MIMO WiMAX-OFDM Based on Wavelet Signals in Transmit-Antenna

This paper investigates a new technique to the adaptation  the Tomlinson-Harashima non-linear  Pre-coding (THP)  in  the WiMAX baseband, in the physical layer performance of multi-antenna techniques, All cases are based on the IEEE 802.16d standard using OFDM based Wavelet and QPSK (¾) of coding rates. The proposed pre-coding only requires the statistical knowledge of the channel at the transmitter, which significantly reduces the feedback requirements. Both linear and non-linear pre-coders amend the system bit error rate for WiMAX OSTBC DWT OFDM in transmit-antenna and path-correlated channels. The proposed non-linear pre-coder in closed loop design achieved much lower bit error rates, increased signal-to-noise power ratio (SNR) than linear pre-coder. Keywords: WiMAX, THP, OFDM, DWT, MIMO, OSTBC

Multi-function RF for Situational Awareness

Radio frequency (RF) communications are an integral part of many situational awareness applications. Sensing data need to be processed in a timely manner, making it imperative to have a robust and reliable RF link for information dissemination. Moreover, there is an increasing need for exploiting RF communication signals directly for sensing, leading to the notion of multi-function RF. In the first part of this dissertation, we investigate the development of a robust Multiple-Input Multiple-Output (MIMO) communication system suitable for airborne platforms.Three majors challenges in realizing MIMO capacity gain in airborne environment are addressed: 1) antenna blockage due largely to the orientation of the antenna array; 2) the presence of unknown interference inherent to the intended application; 3) the lack of channel state information (CSI) at the transmitter. Built on the Diagonal Bell-Labs Layered Space-Time (D-BLAST) MIMO architecture, the system integrates three key design approaches: spatial spreading to counter antenna blockage; temporal spreading to mitigate signal to interference and noise ratio degradation due to intended or unintended interference; and a simple low rate feedback scheme to enable real time adaptation in the absence of full transmitter CSI. Extensive experiment studies using a fully functioning $4\times 4$ MIMO system validate the developed system. In the second part, ambient RF signals are exploited to extract situational awareness information directly. Using WiFi signals as an example, we demonstrate that the CSI obtained at the receiver contains rich information about the propagation environment. Two distinct learning systems are developed for occupancy detection using passive WiFi sensing. The first one is based on deep learning where a parallel convolutional neural network (CNN) architecture is designed to extract useful information from both magnitude and phase of the CSI. Pre-processing steps are carefully designed to preserve human motion induced channel variation while insulating against other impairments and post-processing is applied after CNN to infer presence information for instantaneous motion outputs. To alleviate the need of tedious training efforts involved in deep learning based system, a novel learning problem with contaminated sampling is formulated. This leads to a second learning system: a two-stage solution for motion detection using support vector machines (SVM). A one-class SVM model is first evaluated whose training data are from human free environment only. Decontamination of human presence data using the one-class SVM is done prior to motion detection through a two-class support vector classifier. Extensive experiments using commercial off-the-shelf WiFi devices are conducted for both systems. The results demonstrate that the learning based RF sensing provides a viable and promising alternative for occupancy detection as they are much more sensitive to human motion than passive infrared sensors which are widely deployed in commercial and residential buildings

High Data Rate Wireless Communication Using MIMO

Wireless communication is the most popular and rapidly growing sector of the commu-nication industry. The permitted bandwidth for every service is very limited and the demand of data transferring is increasing day by day. Moreover, the channels are further limited by multipath and fading. Hence, it is a big challenge to provide excellent quality of service and meet the growing demand with the existing bandwidth limitation. MIMO is one very promising technique to enhance the data rate. Fading has been considered as problem for high quality with low outage wireless com-munication. However, multiple-input multiple-output (MIMO) antenna has used this fading phenomenon not only to mitigate the fading but also to exploit this fading to obtain high data rate through spatial multiplexing. In this thesis, MIMO spatial multiplexing has been studied in details. Different MIMO channel models, space time coding, and channel capacity constraints as well as the fac-tors those limits the capacity are studied. One major aim of this study is to find a com-bined optimal solution for MIMO system so that it could provide high rate data transfer.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Low cost passive radar through software defined radio

Passive radars utilise existing terrestrial radio signals, such as those produced by radio or television stations, to track objects within their range. This project aims to determine the suitability of low cost USB TV tuners as hardware receivers for a Software Defined Radio (SDR) based passive radar receiver. Subsequently determining its effectiveness in producing inverse synthetic aperture radar images using data collected from Digital Television signals. Since the initial identification of passive radar, Militaries the world over have been using it as a part of electronic warfare. The evolution of SDR has enabled greater access to the technologies required to implement passive radar, with the greatest limitation being the cost of the required hardware. The availability of low cost hardware was therefore investigated to determine its suitability and subsequently the availability of passive radar to a wider audience. Research was conducted into the available SDR receivers, and comparison of specifications was made against the low cost receiver used in the project. A functional hardware platform based around the Realtek RTL2832U chipset has been developed to determine its suitability as a low cost receiver verifying its ability to coherently receive radio signals for target identification. A complex ambiguity function was implemented to interpret sampled data windows, with the output of these windows to be compared to the requirements for an inverse synthetic aperture radar input, thus determining the suitability of the device. Interpretation of the received data has identified that although the hardware is capable, a real time implementation of data processing is not yet possible, impeding the ability to determine the suitability of the receiver as an inverse synthetic aperture receiver. The results of testing show that the hardware is capable of receiving and producing radar images, however due to the bandwidth of DVB-T signals , and the bandwidth limitations inherent in RTL-SDR dongles, they have proven not to be suitable for DVB-T based inverse synthetic aperture radar receivers