Browsing by Subject "compressive sensing"
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Item Digitally-Assisted Mixed-Signal Wideband Compressive Sensing(2012-07-16) Yu, ZhuizhuanDigitizing 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.Item Phase Retrieval of Sparse Signals from Magnitude Information(2014-07-11) Apaydin, MeltemThe ability to recover the phase information of a signal of interest from a measurement process plays an important role in many practical applications. When only the Fourier transform magnitude of the signal is recorded, recovering the complete signal from these nonlinear measurements turns into a problem of phase retrieval. Many practical algorithms exist to handle the phase retrieval problem. However, they present the drawback of convergence to a local minimum because of the non-convex Fourier magnitude constraints. Recent approaches formulating the problem in a higher dimensional space overcome this drawback but require a sufficiently large number of measurements. By using compressive sensing (CS) techniques, the number of measurements required for phase retrieval can be reduced with the additional information pertaining to the signal structure. With the aim of reducing the number of measurements, this dissertation focuses on the problem of signal recovery by exploiting the sparsity information present in the signal samples. In this thesis, two approaches are proposed to accomplish sparse signal recovery from fewer magnitude measurements, modified Phase Cut and improved Phase Lift. In these approaches, we combine the phase retrieval methods, both Phase Cut and Phase Lift, which formulate the problem in a higher dimensional space, with l_(1)-norm minimization idea in CS by exploiting the sparse structure of the signals. The minimum number of measurements required for signal recovery by the proposed approaches is less than the number that Phase Cut and Phase Lift methods require. Both the modified Phase Cut and the improved Phase Lift approaches outperform another variation of the Phase Lift method, Compressive Phase Retrieval via Lifting; namely, better signal reconstruction rate is obtained for different sparsity degrees. However, in terms of computation time, Phase Lift based methods are faster than the Phase Cut based methods. Ultimately, combining the phase retrieval methods with the l_(1)-norm minimization enables the usage of the sparse structure of the signal for the exact recovery up to a sparsity degree from fewer magnitude measurements. However, challenges remain, particularly those related with computation time of methods and the sparsity degree of the signal which the methods could recover up to by fewer measurements.