Xampling: Signal Acquisition and Processing in Union of Subspaces

We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two. Analog compression that narrows down the input bandwidth prior to sampling with commercial devices. A nonlinear algorithm then detects the input subspace prior to conventional signal processing. A representative union model of spectrally-sparse signals serves as a test-case to study these Xampling functions. We adopt three metrics for the choice of analog compression: robustness to model mismatch, required hardware accuracy and software complexities. We conduct a comprehensive comparison between two sub-Nyquist acquisition strategies for spectrally-sparse signals, the random demodulator and the modulated wideband converter (MWC), in terms of these metrics and draw operative conclusions regarding the choice of analog compression. We then address lowrate signal processing and develop an algorithm for that purpose that enables convenient signal processing at sub-Nyquist rates from samples obtained by the MWC. We conclude by showing that a variety of other sampling approaches for different union classes fit nicely into our framework.Comment: 16 pages, 9 figures, submitted to IEEE for possible publicatio

Digitally-Assisted Mixed-Signal Wideband Compressive Sensing

Digitizing wideband signals requires very demanding analog-to-digital conversion (ADC) speed and resolution specifications. In this dissertation, a mixed-signal parallel compressive sensing system is proposed to realize the sensing of wideband sparse signals at sub-Nqyuist rate by exploiting the signal sparsity. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) front-end, which not only can filter out the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. Moreover, the signal randomization in the system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth rather than concentrate on a few frequencies as it is the case for a conventional ADC. This important new property relaxes the ADC SFDR requirement when sensing frequency-domain sparse signals. The mixed-signal compressive sensing system performance is greatly impacted by the accuracy of analog circuit components, especially with the scaling of CMOS technology. In this dissertation, the effect of the circuit imperfection in the mixed-signal compressive sensing system based on the PSCS front-end is investigated in detail, such as the finite settling time, the timing uncertainty and so on. An iterative background calibration algorithm based on LMS (Least Mean Square) is proposed, which is shown to be able to effectively calibrate the error due to the circuit nonideal factors. A low-speed prototype built with off-the-shelf components is presented. The prototype is able to sense sparse analog signals with up to 4 percent sparsity at 32 percent of the Nqyuist rate. Many practical constraints that arose during building the prototype such as circuit nonidealities are addressed in detail, which provides good insights for a future high-frequency integrated circuit implementation. Based on that, a high-frequency sub-Nyquist rate receiver exploiting the parallel compressive sensing is designed and fabricated with IBM90nm CMOS technology, and measurement results are presented to show the capability of wideband compressive sensing at sub-Nyquist rate. To the best of our knowledge, this prototype is the first reported integrated chip for wideband mixed-signal compressive sensing. The proposed prototype achieves 7 bits ENOB and 3 GS/s equivalent sampling rate in simulation assuming a 0.5 ps state-of-art jitter variance, whose FOM beats the FOM of the high speed state-of-the-art Nyquist ADCs by 2-3 times. The proposed mixed-signal compressive sensing system can be applied in various fields. In particular, its applications for wideband spectrum sensing for cognitive radios and spectrum analysis in RF tests are discussed in this work

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Ultra Wideband

Ultra wideband (UWB) has advanced and merged as a technology, and many more people are aware of the potential for this exciting technology. The current UWB field is changing rapidly with new techniques and ideas where several issues are involved in developing the systems. Among UWB system design, the UWB RF transceiver and UWB antenna are the key components. Recently, a considerable amount of researches has been devoted to the development of the UWB RF transceiver and antenna for its enabling high data transmission rates and low power consumption. Our book attempts to present current and emerging trends in-research and development of UWB systems as well as future expectations

Sparse Array Signal Processing: New Array Geometries, Parameter Estimation, and Theoretical Analysis

Array signal processing focuses on an array of sensors receiving the incoming waveforms in the environment, from which source information, such as directions of arrival (DOA), signal power, amplitude, polarization, and velocity, can be estimated. This topic finds ubiquitous applications in radar, astronomy, tomography, imaging, and communications. In these applications, sparse arrays have recently attracted considerable attention, since they are capable of resolving O(N2) uncorrelated source directions with N physical sensors. This is unlike the uniform linear arrays (ULA), which identify at most N-1 uncorrelated sources with N sensors. These sparse arrays include minimum redundancy arrays (MRA), nested arrays, and coprime arrays. All these arrays have an O(N2)-long central ULA segment in the difference coarray, which is defined as the set of differences between sensor locations. This O(N2) property makes it possible to resolve O(N2) uncorrelated sources, using only N physical sensors. The main contribution of this thesis is to provide a new direction for array geometry and performance analysis of sparse arrays in the presence of nonidealities. The first part of this thesis focuses on designing novel array geometries that are robust to effects of mutual coupling. It is known that, mutual coupling between sensors has an adverse effect on the estimation of DOA. While there are methods to counteract this through appropriate modeling and calibration, they are usually computationally expensive, and sensitive to model mismatch. On the other hand, sparse arrays, such as MRA, nested arrays, and coprime arrays, have reduced mutual coupling compared to ULA, but all of these have their own disadvantages. This thesis introduces a new array called the super nested array, which has many of the good properties of the nested array, and at the same time achieves reduced mutual coupling. Many theoretical properties are proved and simulations are included to demonstrate the superior performance of super nested arrays in the presence of mutual coupling. Two-dimensional planar sparse arrays with large difference coarrays have also been known for a long time. These include billboard arrays, open box arrays (OBA), and 2D nested arrays. However, all of them have considerable mutual coupling. This thesis proposes new planar sparse arrays with the same large difference coarrays as the OBA, but with reduced mutual coupling. The new arrays include half open box arrays (HOBA), half open box arrays with two layers (HOBA-2), and hourglass arrays. Among these, simulations show that hourglass arrays have the best estimation performance in presence of mutual coupling. The second part of this thesis analyzes the performance of sparse arrays from a theoretical perspective. We first study the Cramér-Rao bound (CRB) for sparse arrays, which poses a lower bound on the variances of unbiased DOA estimators. While there exist landmark papers on the study of the CRB in the context of array processing, the closed-form expressions available in the literature are not applicable in the context of sparse arrays for which the number of identifiable sources exceeds the number of sensors. This thesis derives a new expression for the CRB to fill this gap. Based on the proposed CRB expression, it is possible to prove the previously known experimental observation that, when there are more sources than sensors, the CRB stagnates to a constant value as the SNR tends to infinity. It is also possible to precisely specify the relation between the number of sensors and the number of uncorrelated sources such that these sources could be resolved. Recently, it has been shown that correlation subspaces, which reveal the structure of the covariance matrix, help to improve some existing DOA estimators. However, the bases, the dimension, and other theoretical properties of correlation subspaces remain to be investigated. This thesis proposes generalized correlation subspaces in one and multiple dimensions. This leads to new insights into correlation subspaces and DOA estimation with prior knowledge. First, it is shown that the bases and the dimension of correlation subspaces are fundamentally related to difference coarrays, which were previously found to be important in the study of sparse arrays. Furthermore, generalized correlation subspaces can handle certain forms of prior knowledge about source directions. These results allow one to derive a broad class of DOA estimators with improved performance. It is empirically known that the coarray structure is susceptible to sensor failures, and the reliability of sparse arrays remains a significant but challenging topic for investigation. This thesis advances a general theory for quantifying such robustness, by studying the effect of sensor failure on the difference coarray. We first present the (k-)essentialness property, which characterizes the combinations of the faulty sensors that shrink the difference coarray. Based on this, the notion of (k-)fragility is proposed to quantify the reliability of sparse arrays with faulty sensors, along with comprehensive studies of their properties. These novel concepts provide quite a few insights into the interplay between the array geometry and its robustness. For instance, for the same number of sensors, it can be proved that ULA is more robust than the coprime array, and the coprime array is more robust than the nested array. Rigorous development of these ideas leads to expressions for the probability of coarray failure, as a function of the probability of sensor failure. The thesis concludes with some remarks on future directions and open problems.</p

Proceedings of the Fifth International Mobile Satellite Conference 1997

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial communications services. While previous International Mobile Satellite Conferences have concentrated on technical advances and the increasing worldwide commercial activities, this conference focuses on the next generation of mobile satellite services. The approximately 80 papers included here cover sessions in the following areas: networking and protocols; code division multiple access technologies; demand, economics and technology issues; current and planned systems; propagation; terminal technology; modulation and coding advances; spacecraft technology; advanced systems; and applications and experiments