Compressed Sensing Receivers: Theory, design, and performance limits.

摘要

The past 50 years have seen tremendous developments in electronics due to the rise and rapid development of IC-fabrication technology [1]. In addition to the production of cheap and abundant computing resources, another area of rapid advancement has been wireless technologies. While the central focus of wireless research has been mobile communication, an area of increasing importance concerns the development of sensing/spectral applications over bandwidths exceeding multiple GHz. Such systems have many applications ranging from scientific to military. Although some solutions exist, their large size, weight, and power make more-efficient solutions desirable.;At present, one of the principal bottlenecks in designing such systems is the power consumption of the back-end ADCs at the required digitization rate. ADCs are a dominant source of power consumption; it is also often the case that ADC block specifications are used to determine parameters for the rest of the signal chain, such as the RF front-end and the DSP-core which processes the digitized samples [2]. Historically, increases in system bandwidth have come from developing ADCs with superior performance.;In contrast to improving ADC performance, this work presents a system-level approach with the goal of minimizing the required digitization rate for observation of a given effective instantaneous bandwidth (EIBW). The approach was inspired by the field of compressed sensing [3--5]. Loosely stated, CS asserts that samples which represent random projections can be used to recover sparse and/or compressible signals with what was previously thought to be insufficient information. The research in this thesis bridges the disparate areas of RF/Mixed-Signal IC design and CS; the primary contributions of this thesis include: the establishment of physical feasibility of CS-based receivers through implementation of the first-ever fully-integrated high speed CS-based front-end known as the random-modulation pre-integrator (RMPI) [6--9], and the development of a principled design methodology based on a rigorous analytical and empirical study of the system.;The 8-channel RMPI was implemented in 90 nm CMOS and was validated by physical measurements of the fabricated chip. The implemented RMPI achieves an EIBW of 2 GHz, with > 54 dB of dynamic range. Most notably, the aggregate digitization rate is fagg = 320 Msps, 12:5x lower than the Nyquist rate.;[1] G. E. Moore, "Cramming more components onto integrated circuits," Electronics, vol. 38, no. 8, pp. 114--117, April 19, 1965. [2] B. Murmann, "Trends in low-power, digitally assisted A/D conversion," IEICE Trans. Electron., vol. E93-C, no. 6, pp. 718--727, 2010. [3] E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489--509, 2006. [4] D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289--1306, 2006. [5] E. J. Candes, "Compressive sampling," Int. Cong. Math., vol. 3, pp. 1433--1452, 2006. [6] J. Yoo, S. Becker, M. Loh, M. Monge, E. Candes, and A. Emami-Neyestanak, "Design and implementation of a fully integrated compressed-sensing signal acquisition system," in Proc. Int. Conf. on Acoustics Speech and Sig. Processing , Kyoto, Japan, 2012. [7] J. Yoo, S. Becker, M. Loh, M. Monge, E. Candes, and A. Emami-Neyestanak, "A 10M--2GHz 12.5x sub-Nyquist rate receiver in 90nm CMOS," Proc. of IEEE Radio Freq. Integrated Cir. Conf. (RFIC), accepted for presentation, 2012. [8] J. N. Laska, S. Kirolos, M. F. Duarte, T. Ragheb, R. G. Baraniuk, and Y. Massoud, "Theory and implementation of an analog-to-information converter using random demodulation," in Proc. IEEE ISCAS, New Orleans, May 2007, pp. 1959--1962. [9] J. A. Tropp, J. N. Laska, M. F. Duarte, J. Romberg, and R. G. Baraniuk, "Beyond Nyquist: Efficient sampling of sparse bandlimited signals," IEEE Trans. Inform. Theory, vol. 56, no. 1, pp. 520--544, 2010.