Browsing by Author "El Smaili, Sami"

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    Efficient Architectures for Wideband Receivers
    (2012-08-29) El Smaili, Sami; Varman, Peter J.; Massoud, Yehia; Clark, John W., Jr.; Koushanfar, Farinaz
    Reducing power consumption of radio receivers is becoming more critical with the advancement of biomedical portable and implantable devices due to the stringent power requirements in such applications. Compressive sensing promises to tremendously reduce the power of radio receivers by allowing the reconstruction of sparse signals from measurements acquired at a sub-Nyquist rate. A key component in compressive sensing systems is the random signal which is used to acquire the measurements. Most e orts have been devoted to the design of signals with high randomness but little have been devoted to manipulating the random signal to suite a speci fic application, meet certain specifi cations, or enhance the performance of the system. This thesis tackles compressive sensing systems from this angle. We first propose an architecture that alleviates a critical requirement in compressive sensing: that the random signal should run at the Nyquist rate, which becomes prohibitive as the signal bandwidth increases. We provide theoretical and experimental results that demonstrate the e ectiveness of the proposed architecture. Secondly, we propose a framework for manipulating the random signal in the frequency domain as suitable for speci c applications. We use the framework to develop an architecture for recon gurable ultra wide-band radios.
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    Random Acquisition Sensors and Receivers: Design, Architectures, and Applications
    (2017-08-07) El Smaili, Sami; Baraniuk, Richard G
    The emergence of concepts such as the internet of things (IoT) is but a manifestation of the increasing integration of communication and data acquisition systems in a wide range of devices and in a plethora of applications. Pushing the limits of traditional Nyquist-based systems, such widespread integration of acquisition systems is coupled with the increasing demand for lower power, higher bandwidth, multi-standard and reconfigurable systems. Compressive sensing promises to alleviate much of the constraints facing Nyquist-based systems and provide efficient solutions in all these areas. However, the current approaches for implementing compressive sensing suffer from several limitations: - A gap between theory and application: reconstruction algorithms use an ideal model of the system that the hardware is designed to mimic. However, in most cases, the ideal model might not be the only one that guarantees reconstruction accuracy, which results in an unduly constrained design. In fact, the theory frames the condition for successful reconstruction in terms of the very general concept of restricted isometry property (RIP) that can be applied to any system model. The ideality of the ideal model does not necessarily has its roots originating in the theory; a more practical model that is easier to mimic can still satisfy the RIP and be considered ideal. - General and widely used architectures have as parameters the number of acquired measurements and signal sparsity. Because these parameters are defined by the application rather than designed, there is little room to tweak the design, at the system level, to manage various system trade-offs and to overcome practical challenges such as the resetting frequency of an integrator or the window length in each projection channel. - Applicability to realms beyond that of sparse signals: In many communication applications, signals are not sparse (and should not be for bandwidth utilization), but random acquisition architecture can still provide tremendous benefits and provide novel solutions. Reaping the benefits of random acquisition in such domains requires new architectures that go beyond the traditional application scope of compressive sensing. The work we propose as part of this thesis aims at overcoming these limitations and expanding the realm of compressive sensing into new areas and applications. In this work we - Provide a practical approach to compressive sensing that starts from a practical system model to derive system requirements for successful reconstruction. We consider the basic building block of compressive sensing, the projection channel, and assume a general filter is used rather than an integrator (the ideal model). We derive the conditions that such a model should have to satisfy the restricted isometry property and show that the new requirements are far less restrictive and more practical than traditional requirements stemming from an integrator-based model. - Develop an approach for quantifying hardware variability and model uncertainty and its effect on reconstruction accuracy. Particularly, we study the effect of filter pole variability on reconstruction, which might be due to model approximations or hardware variability. - Propose a multi-channel random demodulator that bridges the gap between the two main architectures, the random demodulator consisting of one projection channel, and the random modulator pre integrator, which uses M channels for M measurements. The multi-channel random demodulator has the number of channels as a design parameter, which can be used to manage practical trade-offs such as the integrator reset frequency and the window length in each channel. We utilize this architecture in the reconfigurable receiver architecture that we also present in this work. - Develop a framework for random acquisition reconfigurable receivers, which expands the realm of compressive sensing to communication systems where signals are dense but their supports are known. We develop a design methodology for such systems, linking the various system parameters to system metrics. - Propose and analyze an ADC-less architecture for sensors that breaks from the conventional compressive sensing approach of digitizing measurements at the acquisition site. We study when this approach is more beneficial than the traditional approach of digitizing on the acquisition site.