237 research outputs found
Energy-Efficient Spectrum Sensing for Cognitive Radio Networks
No full textDynamic spectrum access employing cognitive radios has been proposed, in order to opportunistically use underutilized spectrum portions of a heavily licensed electromagnetic spectrum. Cognitive radios opportunistically share the spectrum, while avoiding any harmful interference to the primary licensed users. One major category of cognitive radios consists of is interweave cognitive radios. In this category, cognitive radios employ spectrum sensing to detect the empty bands of the radio spectrum, also known as spectrum holes. Upon detection of such a spectrum hole, cognitive radios dynamically share this empty band. However, as soon as the primary user appears in the corresponding band, cognitive radios have to vacate the band and look for a new spectrum hole. This way, reliable spectrum sensing becomes a key functionality of a cognitive radio network. The hidden terminal problem and fading effects have been shown to limit the reliability of spectrum sensing. Distributed cooperative detection has therefore been proposed to improve the detection performance of a cognitive radio network. In this thesis, a distributed detection scheme based on hard fusion of local results is considered. Each cognitive radio senses the spectrum and sends the result to the fusion center, and there the final decision is made about the presence or absence of the primary user. Note that, in general, cognitive radios are low-power sensors and thus energy consumption becomes a critical issue. In this thesis, several energy-efficient approaches are proposed, in order to minimize the maximum average energy consumption per sensor, while satisfying the sensing reliability of the cognitive radio network. The sensing reliability is defined by a lower bound on the probability of detection and an upper bound on the probability of false alarm. This way, the primary user is protected from the cognitive radio transmitter’s interference and also the chance of losing spectrum access through erroneous detection of the primary user in an empty band is constrained. First, a censoring scheme is considered where cognitive radios send their results to the fusion center only if they are deemed to be informative. Second, a combined censoring and truncated sequential sensing scheme is depicted which is shown to be more energy-efficient than the former case due to the sensing energy reduction. And third, a combined censoring and sleeping scheme is discussed where on top of censoring, each cognitive radio switches off its sensing module with a specific sleeping rate, in order to save energy both on transmission and sensing. It is shown that all the proposed schemes, particularly combined censoring and sleeping as well as censored truncated sequential sensing delivers significant energy savings. Further, we conclude that when a cognitive radio system is appropriately well-designed in terms of energy efficiency, increasing the number of cooperative cognitive sensors, not only improves the detection performance, but also reduces the average energy consumption of individual cognitive radios. Finally, an optimal fusion strategy for energy-constrained hard-fusion based cognitive radio networks is presented, which optimizes the network throughput subject to a constraint on the average energy consumption of individual radios and a constraint on the amount of interference to the primary user. It is shown that the majority rule is either optimal or close to optimal in terms of the network throughput.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc
Compressive Sampling for Wireless Communications
No full textWireless communications is undergoing massive development in all forms of its manifestations. In the field of short-range communications, technologies like ultra- wideband (UWB) systems are promising very high data rates, fine timing resolution and coexistence with other physical layer standards. Along with these benefits, the promise of low-cost and low-complexity devices makes UWB systems a highly sought-after option. The main reason for these benefits is the utilization of a very large bandwidth. However, these benefits come at a price, that is the high sampling rate required to receive such signals. According to the Nyquist sampling theorem, a signal can be fully determined if sampled at twice its maximum frequency. This means that the UWB signals may require a sampling rate in the order of Giga samples per second. At the receiver, the sampling is carried out by an analog-to-digital converter (ADC). The power consumption of an ADC is proportional to its sampling rate. A very high sampling rate means stressing the ADC in terms of power consumption. This can put the whole idea of low-cost and low-complexity UWB systems in jeopardy. Therefore, using subsampling methods is indispensable. In this regard, we propose the utilization of compressive sampling (CS) for UWB systems. CS promises a reasonable reconstruction performance of the complete signal from very few compressed samples, given the sparsity of the signal. In this thesis, we concentrate on impulse-radio (IR) UWB systems. IR-UWB signals are known to be sparse, meaning, a large part of the received signal has zero or insignificant components. We exploit this time-domain sparsity and reduce the sampling rate much below the Nyquist rate but still develop efficient detectors. We propose CS-based energy detectors for IR-UWB pulse position modulation (PPM) systems in multipath fading environments. We use the principles of generalized maximum likelihood to propose detectors which require the reconstruction of the original signal from compressed samples and detectors which skip this recon- struction step and carry out detection on the compressed samples directly, thereby further reducing the complexity. We provide exact theoretical expressions for the bit error probability (BEP) to assess the performance of our proposed detectors. These expressions are further verified by numerical simulations. We also propose CS-based differential detectors for IR-UWB signals. These detectors work on consecutive symbols. We develop detectors with separate recon- struction and detection stages as well as detectors that perform these steps jointly. We further present detectors which do not need reconstruction at all and can work directly on the compressed samples. However, this can put some limitations on the overall flexibility of the detector in terms of the measurement process. To assess the performance of all these detectors, we also provide maximum a posteriori (MAP) based detectors. We provide numerical simulations to display the detection results. We extend the CS-based classical differential detectors to the case of multiple symbol differential detectors. To keep the implementation complexity at its min- imum, we work only with compressed samples directly. We use the principles of the generalized likelihood ratio test (GLRT) to eliminate the limitations on such de- tectors, in terms of the measurement process. Apart from focusing on compressed detectors which contain full timing information, we also propose detectors which need such information at symbol level only. This effectively results in low-cost and low-complexity detectors. Finally, we present some work on the theoretical aspects of CS. We develop algorithms which exploit the block sparse structure of the signal. This block spar- sity is combined with varying block sizes and signal coefficients having smooth transitions. Such signals are often encountered in a wide range of engineering and biological fields.Circuits and SystemsElectrical Engineering, Mathematics and Computer Scienc
Compressive Power Spectral Analysis
No full textAt the heart of digital signal processing (DSP) are the sampling and quantization processes, which convert analog signals into discrete samples and which are implemented in the form of analog to digital converters (ADCs). In some recent applications, there is an increased demand for DSP applications to process signals having a very wide bandwidth. For such signals, the minimum allowable sampling rate is also very high and this has put a very high demand on the ADCs in terms of power consumption. Recently, the emergence of compressive sampling (CS) has offered a solution that allows us to reconstruct the original signal from samples collected from a sampling device operating at sub-Nyquist rate. The application of CS usually involves applying an additional constraint such as a sparsity constraint on the original signal. However, there are also applications where the signal to deal with has a high bandwidth (and thus sub-Nyquist rate sampling is still important) but where only the second-order statistics (instead of the original signal) are required to be reconstructed. In the latter case, depending on the characteristics of the signals, it might be possible to reconstruct the second-order statistics of the received analog signal from its sub-Nyquist rate samples without applying any additional constraints on the original signals. This idea is the key starting point of this thesis. We first focus on time-domain wide-sense stationary (WSS) signals and introduce a method for reconstructing their power spectrum from their sub-Nyquist rate samples without requiring the signal or the power spectrum to be sparse. Our method is examined both in the time- and frequency-domain and the solution is computed using a simple least-squares (LS) approach, which produces a solution if the rank condition of the resulting system matrix is satisfied. To satisfy this rank condition, two options of sampling design are proposed, one of which is the so-called multi-coset sampling. It is show in this thesis that any of the so-called sparse ruler can produce a multi-coset sampling design that guarantees the full rank condition of the system matrix, and thus the optimal compression is achieved by a minimal sparse ruler. While the approach in the previous paragraph is related to time-domain signals, we could extend the discussion about the power spectrum reconstruction from sub-Nyquist rate samples in the context of the spatial-domain signal, which is defined as a sequence of outputs of the antennas in the antenna array at a particular time instant. Given the compressed spatial domain signals, which are obtained from the output of a uniform linear array (ULA) with some antennas turned off, of particular interest is to reconstruct the angular power spectrum, from which the direction of arrival (DOA) of the sources can generally be located. In this thesis, a method to estimate the angular power spectrum and the DOA of possibly fully correlated sources based on second-order statistics of the compressed spatial-domain signals is proposed by employing a so-called dynamic array which is built upon the so-called underlying ULA. In this method, we present the spatial correlation matrices of the output of the dynamic active antenna arrays at all time slots as a linear function of the spatial correlation matrix of the entire underlying uniform array and we solve for this last correlation matrix using LS. The required theoretical condition to ensure the full column rank condition of the system matrix is formulated and designs are proposed to satisfy this condition. Next, we consider both spatio-angular and time-frequency domains and propose a compressive periodogram reconstruction method as our next contribution. We introduce the multibin model, where the entire band is divided into equal-size bins such that the spectra at two frequencies or angles, whose distance is at least equal to the bin size, are uncorrelated. This model results in a circulant structure in the so-called coset correlation matrix, which enables us to introduce a strong compression. We propose the sampling patterns based on a circular sparse ruler to guarantee the full column rank condition of the system matrix and to allow the LS reconstruction of the periodogram. We also provide a method for the case when the bin size is reduced such that the spectra at two frequencies or angles, whose distance is larger than the bin size, can still be correlated. To combine frequency and DOA processing, we also introduce a compressive two-dimensional (2D) frequency- and angular-domain power spectrum reconstruction for multiple uncorrelated time-domain WSS signals received from different sources by a linear array of antennas. We perform spatial-domain compression by deactivating some antennas in an underlying ULA and time-domain compression by multi-coset sampling. Finally, we propose a compressive cyclic spectrum reconstruction approach for wide-sense cyclostationary (WSCS) signals, where we consider sub-Nyquist rate samples produced by non-uniform sampling. This method is proposed after first observing that the block Toeplitz structure emerges in theWSCS signal correlation matrix. This structure is exploited to solve the WSCS signal correlation matrix by LS. The condition for the system matrix to have full column rank is provided and some possible non-uniform sampling designs to satisfy this full column rank condition are presented. Based on all the works that have been done, we have found that focusing on reconstructing the statistical measure of the received signals has significantly relax the sampling requirements and the constraints on both the statistics and the signals themselves. Hence, we would like to conclude that, for given tasks of applications in hand, we should ask ourselves whether statistical measure reconstruction is sufficient since the answer for this question will likely to determine how we should collect the data from the observed phenomena. This underlines the importance of awareness on what kind of information is necessary and sufficient for the tasks in hand before conducting the sensing/sampling process.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc
Single-carrier block transmission for underwater communications
No full textThe present report assesses the performance of several receiver schemes that attempt to recover as best as possible data transmitted through an acoustic underwater channel modulating a low-frequency single-carrier wave at a high data-rate for this kind of medium. The shifted Known Symbol Padding block structure is considered robust against highly variable underwater channels, which cause severe syn- chronization problems due to significant Doppler spreading. The cho- sen receiver comprises a channel estimation stage which is based on variable training sequences. Three distinct methods are compared, and provide a basis for equalization filtering. The latter is performed on the received data to estimate the transmitted message, and it is performed both in the time-domain and in the frequency-domain in order to assess which approach delivers the best results. All of the above methods assume a time varying channel impulse response. The results obtained are later compared with a Decision Feedback Equal- izer in order to conclude if they are a reliable alternative to it.MSc Electrical Engineering - TelecommunicationsCircuits and SystemsElectrical Engineering, Mathematics and Computer Scienc
A multi-lag/multi-scale receiver for underwater acoustic communications
No full textWireless communications have numerous applications in terrestrial and space environ-ments, but for one environment the number of civil wireless applications is small. This is the uderwater environment where for wireless communications acoustic waves are used instead of electromagnetic waves. The underwater acoustic channel is a very difficult medium for wireless communications and is subject to severe multipath and Doppler effects. It is possible that each multipath component may have a unique delay and Doppler shift, so called multi-scale/multi-lag channels. This is at present a limiting factor for wireless communications underwater. At the moment always a single Doppler rate is assumed which converts to a narrowband system when re-sampled. In this report a receiver is introduced which can remove the effects of multipath and Doppler where the novelty lies in the fact that it is especially designed for a multi-scale/multi-lag channel. This is done by introducing equalizers followed by channel estimation which are both designed for a multi-scale/multi-lag channel. The designed receiver shows promising results and is able to recover the original transmitted symbols according to the simulations.Signal Proccessing for the CommunicationsCircuits and SystemsElectrical Engineering, Mathematics and Computer Scienc
Non-Intrusive Appliance Load Monitoring using the Viterbi Algorithm (NIALM-VA)
No full textThe goal of Non-Intrusive Appliance Load Monitoring (NIALM), or energy disaggregation, is to deduce which devices are active and how much energy they consume from observation of the time evolution of the total voltage or current in the electrical network. In this thesis, energy disaggregation is performed from the time series of power meter readings, by making use of the fact that different types of appliance can be distinguished by the statistical properties of their signatures, i.e., power consumption behaviour over time. Optimal detectors, using the Viterbi algorithm, are derived for three types of signature: 1) only power levels, no time information, 2) power levels with exponentially distributed lifetimes and 3) power levels with Gaussian lifetimes with given means and variances. The detectors have been implemented in MATLAB, and their performance is compared for various inputs.TelecommunicationsElectrical Engineering, Mathematics and Computer Scienc
Acoustic Vector Sensor Based Source Localization
No full textThe conventional techniques to estimate the Direction-of-Arrival (DOA) of acoustic sources includes employing multiple Acoustic Pressure Sensors (APSs) in an array configuration. Recently with the advent of MEMS technology, the sensors are capable of capturing acoustic particle velocity along with acoustic pressure and these sensors are referred to as Acoustic Vector Sensors (AVSs). In order to capture the velocity field information, AVSs contain two (three) vector sensors which are aligned along corresponding coordinate axes in the two (three) dimensional acoustic space. Based on narrow-band processing techniques and for a single far-field source scenario, we discuss the advantages obtained by an AVS array in comparison to a conventional APS array based on their respective beam patterns. Later the discussion is extended to a configuration where each AVS in the array contains only one vector sensor (which measures one component of the vector field), such that it can have a variable orientation, and a pressure sensor, which is referred to as a 1D AVS. We discuss the similarities and differences between 2D AVS arrays and 1D AVS arrays through beam patterns. Also we discuss the bias in the source location estimate obtained by using a 1D AVS array. Further we derive Cramer-Rao bounds for 2D AVS, 1D AVS and APS array configurations. Finally we compare the performance of 1D AVS arrays and 2D AVS arrays in the covariance domain in terms of the localization of quasi-stationary sources.Electrical Engineering, Mathematics and Computer ScienceMicroelectronicsCircuits and System
Ultrasound Imaging Using a Single Element Transducer
No full textElectrical Engineering, Mathematics and Computer ScienceMicroelectronicsCircuits and System
Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications
No full textIn today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop a data reduction tool called sparse sensing, which consists of a deterministic and structured sensing function (guided by a sparse vector) that is optimally designed to achieve a desired inference performance with the reduced number of data samples. The first part of this thesis is dedicated to the development of sparse sensing mechanisms and convex programs to efficiently design sparse sensing functions. We design sparse sensing functions under the assumption that the data is not yet available and the model information is perfectly known. Sparse sensing offers a number of advantages over compressed sensing (a state-of-the-art data reduction method for sparse signal recovery). One of the major differences is that in sparse sensing the underlying signals need not be sparse. This allows us to consider general signal processing tasks (not just sparse signal recovery) under the proposed sparse sensing framework. Specifically, we focus on fundamental statistical inference tasks, like estimation, filtering, and detection. In essence, we present topics that transform classical (e.g., random or uniform) sensing methods to low-cost data acquisition mechanisms tailored for specific inference tasks. The developed framework can be applied to sensor selection, sensor placement, or sensor scheduling, for example. In the second part of this thesis, we focus on some applications related to distributed sampling using sensor networks. Sensor networks can be used as a spatial sampling device, that is, to faithfully represent distributed signals (e.g., a spatially varying phenomenon such as a temperature field). On top of that, the distributed signals can exist in space and time, where the temporal sampling is achieved using analog-to-digital converters, for example. Each sensor has an independent sample clock, and its stability essentially determines the alignment of the temporal sampling grid across the sensors. Due to imperfections in the oscillator, the sample clocks drift from each other, resulting in the misalignment of the temporal sampling grids. To overcome this issue, we devise a mechanism to distribute the sample clock wirelessly. Specifically, we perform wireless clock synchronization based on the time-of-flight measurements of broadcast messages. In addition, clock synchronization also plays a central role in other time-based sensor network applications such as localization. Localization is increasingly gaining popularity in many applications, especially for monitoring environments beyond human reach, e.g., using robots or drones with several sensor units mounted on it. Consequently we now have to localize more than one sensor or even localize the whole sensing platform. Therefore, we extend the classical localization paradigm to localize a (rigid) sensing platform by exploiting the knowledge of the sensor placement on the platform. In particular, we develop algorithms for rigid body localization, i.e., for estimating the position and orientation of the rigid platform using distance measurements. Given the central role of sensing and sensor networks, the results presented in this thesis impacts a wide range of applications.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc
Signal strength based localization and path-loss exponent self-estimation in wireless networks
No full textWireless communications and networking are gradually permeating our life and substantially influencing every corner of this world. Wireless devices, particularlythose of small size, will take part in this trend more widely, efficiently, seamlessly and smartly. Techniques requiring only limited resources, especially in terms of hardware, are becoming more important and urgently needed. That is why we focus this thesis around analyzing wireless communications and networking based on signal strength (SS) measurements, since these are easy and convenient to gather. SS-based techniques can be incorporated into any device that is equipped with a wireless chip
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