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Concept# Ultra-wideband

Summary

Ultra-wideband (UWB, ultra wideband, ultra-wide band and ultraband) is a radio technology that can use a very low energy level for short-range, high-bandwidth communications over a large portion of the radio spectrum. UWB has traditional applications in non-cooperative radar imaging. Most recent applications target sensor data collection, precise locating, and tracking. UWB support started to appear in high-end smartphones in 2019.
Characteristics
Ultra-wideband is a technology for transmitting information across a wide bandwidth (>500 MHz). This allows for the transmission of a large amount of signal energy without interfering with conventional narrowband and carrier wave transmission in the same frequency band. Regulatory limits in many countries allow for this efficient use of radio bandwidth, and enable high-data-rate personal area network (PAN) wireless connectivity, longer-range low-data-rate applications, and the transparent co-existence of radar and imaging syste

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Sampling theory has experienced a strong research revival over the past decade, which led to a generalization of Shannon's original theory and development of more advanced formulations with immediate relevance to signal processing and communications. For example, it was recently shown that it is possible to develop exact sampling schemes for a large class of non-bandlimited signals, namely, certain signals with finite rate of innovation. A common feature of such signals is that they have a parametric representation with a finite number of degrees of freedom and can be perfectly reconstructed from a finite number of samples. The goal of this thesis is to advance the sampling theory for signals of finite rate of innovation and consider its possible extensions and applications. In the first part of the thesis, we revisit the sampling problem for certain classes of such signals, including non-uniform splines and piecewise polynomials, and develop improved schemes that allow for stable and precise reconstruction in the presence of noise. Specifically, we develop a subspace approach to signal reconstruction, which converts a nonlinear estimation problem into the simpler problem of estimating the parameters of a linear model. This provides an elegant and robust framework for solving a large class of sampling problems, while offering more flexibility than the traditional scheme for bandlimited signals. In the second part of the thesis, we focus on applications of our results to certain classes of nonlinear estimation problems encountered in wideband communication systems, most notably ultra-wideband (UWB) systems, where the bandwidth used for transmission is much larger than the bandwidth or rate of information being sent. We develop several frequency domain methods for channel estimation and synchronization in UWB systems, which yield high-resolution estimates of all relevant channel parameters by sampling a received signal below the traditional Nyquist rate. We also propose algorithms that are suitable for identification of more realistic UWB channel models, where a received signal is made up of pulses with different pulse shapes. Finally, we extend our results to multidimensional signals, and develop exact sampling schemes for certain classes of parametric non-bandlimited 2-D signals, such as sets of 2-D Diracs, polygons or signals with polynomial boundaries.

This thesis evaluates the potential of Ultra Wideband Impulse Radio for wireless sensor network applications. Wireless sensor networks are collections of small electronic devices composed of one or more sensors to acquire information on their environment, an energy source (typically a battery), a microcontroller to control the measurements, process the information and communicate with its peers, and a radio transceiver to enable these communications. They are used to regularly collect information within their deployment area, often for very long periods of time (up to several years). The large number of devices often considered, as well as the long deployment durations, makes any manual intervention complex and costly. Therefore, these networks must self-configure, and automatically adapt to changes in their electromagnetic environment (channel variations, interferers) and network topology modifications: some nodes may run out of energy, or suffer from a hardware failure. Ultra Wideband Impulse Radio is a novel wireless technology that, thanks to its extremely large bandwidth, is more robust to frequency dependent propagation effects. Its impulsional nature makes it robust to multipath fading, as the short duration of the pulses leads most multipath components to arrive isolated. This technology should also enable high precision ranging through time of flight measurements, and operate at ultra low power levels. The main challenge is to design a system that reaches the same or higher degree of energy savings as existing narrowband systems considering all the protocol layers. As these radios are not yet widely available, the first part of this thesis presents Maximum Pulse Amplitude Estimation, a novel approach to symbol-level modeling of UWB-IR systems that enabled us to implement the first network simulator of devices compatible with the UWB physical layer of the IEEE 802.15.4A standard for wireless sensor networks. In the second part of this thesis, WideMac, a novel ultra low power MAC protocol specifically designed for UWB-IR devices is presented. It uses asynchronous duty cycling of the radio transceiver to minimize the power consumption, combined with periodic beacon emissions so that devices can learn each other's wake-up patterns and exchange packets. After an analytical study of the protocol, the network simulation tool presented in the first part of the thesis is used to evaluate the performance of WideMac in a medical body area network application. It is compared to two narrowband and an FM-UWB solutions. The protocol stack parameters are optimized for each solution, and it is observed that WideMac combined to UWB-IR is a credible technology for such applications. Similar simulations, considering this time a static multi-hop network are performed. It is found that WideMac and UWB-IR perform as well as a mature and highly optimized narrowband solution (based on the WiseMAC ULP MAC protocol), despite the lack of clear channel assessment functionality on the UWB radio. The last part of this thesis studies analytically a dual mode MAC protocol named WideMac-High Availability. It combines the Ultra Low PowerWideMac with the higher performance Aloha protocol, so that ultra low power consumption and hence long deployment times can be combined with high performance low latency communications when required by the application. The potential of this scheme is quantified, and it is proposed to adapt it to narrowband radio transceivers by combining WiseMAC and CSMA under the name WiseMAC-HA.

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In this thesis, we focus on Impulse Radio (IR) Ultra-WideBand (UWB) ranging and positioning techniques under indoor propagation environments. IR-UWB, a new carrierless communication scheme using impulses, is a candidate technology for future communication, ranging and positioning applications. Recent progress on both the technical and regulatory side of this technology has made this possible [1][2][3]. The fine time resolution of UWB signals has created a vision of novel ranging and positioning applications to augment existing narrowband systems operating in dense multipath environments [4][5][6]. We propose a high-resolution IR-UWB ranging algorithm based on Maximum Likelihood (ML) when the noise is additive Gaussian noise or multi-user interference. First, we pose the ranging problem as an ML estimation problem for the channel delays and their amplitudes at the receiver. We evaluate the ranging by translating the received delay estimates into an estimate of the distance. Then, we use the same transformation as the first step of Iterative Quadratic Maximum Likelihood (IQML) and we transform the ML problem into another optimization problem that avoids the estimation of the amplitude coefficients. We solve the remaining optimization problem with a gradient descent approach (Pseudo-Quadratic Maximum Likelihood (PQML) algorithm). Most previous works assume the distribution of the targeted distance is a uniform distribution (for example [7], [8] and [9]). In contrast to the previous works, we propose that the distribution of the targeted distance, which is not necessarily a uniform distribution, should depend on the geometry of the indoor environments of interest. We propose a Bayesian detection algorithm where the prior distribution of the channel follows the IEEE 802.15.4a channel model to identify whether the received signal is Line-Of-Sight (LOS) signal or a Non-Line-Of-Sight (NLOS) signal. If it is a LOS signal, we use a Bayesian estimation approach to estimate the joint posterior probability density function (pdf) of the channel and the targeted distance. We use this pdf function to estimate the ranging with Minimum Mean Square Error Estimator (MMSE). For computing the joint posterior pdf of the channel and the targeted distance, we derive a novel algorithm based on Sampling and Importance Resampling (SIMR) and Expectation Maximum (EM) techniques. Furthermore, we propose a reduced-complexity architecture of an IR-UWB ranging system by using our proposed algorithms. We also implement the Bayesian ranging algorithm on the Xinlix ML410 Field-Programmable Gate Array (FPGA) board. The used FPGA resources show that the Bayesian ranging algorithm is a low-complexity algorithm. We derive a novel Ziv-Zakai lower bound for IR-UWB ranging error that depends on the geometry of the indoor environments of interest. In contrast to the work in [7], we do not introduce, during our derivation process, any approximation for our log-likelihood function. Therefore, we obtain a more accurate Ziv-Zakai lower bound for the IR-UWB ranging error with IEEE 802.15.4a channel models. Based on the geometry of the indoor environments of interest, we can find the "best" position of the base station that provides the lowest Ziv-Zakai lower bound of the IR-UWB ranging error. Our bound can also be used in real environments with the channel measurements from real environments. We also propose that the distribution of the targeted position should depend on the geometry of the indoor environments of interest. We propose a novel one-step approach that estimates the position and channels directly and jointly from the received signals of the used base stations. We use a Bayesian approach where the prior distribution of the channels follows the IEEE 802.15.4a channel model to estimate the joint posterior pdf of the channels, the targeted position and the transmit time. One application of the joint posterior pdf of the channels, the targeted position and the transmit time is the determination of the position with classical posterior estimator (such as MMSE). For computing the joint posterior pdf of the channels, the targeted position and the transmit time, we derived an algorithm that is based on SIMR and EM techniques. We derive Ziv-Zakai position error lower bounds for a one-step positioning scheme, a Time Of Arrival (TOA)-based positioning scheme and a Time Difference Of Arrival (TDOA)-based positioning scheme. As our derived bounds depend on the geometry of the indoor environments of interest, our bounds can also be used in real environments with the channel measurements from real environments. Multi-User Interference (MUI) statistical models for IR-UWB systems can be important in providing an accurate estimate of the channel state. As such, it can have a major impact on the overall system performance. In the literature, MUI in the time domain is often approximated with Middleton class A and Gaussian Mixture Models. We use measurements from an indoor IR-UWB testbed to assess the validity of these models. We analyzed the statistical properties of IR-UWB MUI with the measurements from real indoor environments.