2019 Theses Doctoral
A Flexible RFIC Architecture for High-Sensitivity Reception and Compressed-Sampling Wideband Detection
Compressed sensing (CS) is a new signal processing approach that has disrupted the Shannon-Nyquist limit based design methodology and has opened promising avenues for building energy-efficient radio frequency integrated circuits (RFICs) for detecting and estimating particular classes (i.e. sparse) of signals. Whether in application domains where naturally occurring signals are sparse or where representations of signals subject to the fidelity limits or configuration settings of the radio equipment are often found to be sparse, the emergence of CS has forced us to re-imagine the radio receiver. While realizing some of the potential benefits promised by theory, CS-RFIC architectures proposed in earlier research were not particularly suitable for mass-market applications.
This thesis demonstrates how to take a new signal processing technique all the way to the hardware level. So far, the main focus in literature has been how CS offers a significant advantage for signal processing. This work will show how CS techniques drive novel architectures down to the integrated circuit level. This requires close collaboration between communication system developers, integrated circuit designers and signal processing experts. The trans-disciplinary approach presented here has led to the unification of CS-inspired architectures for wideband signal detection with robust, legacy architectures for high-sensitivity signal reception. The result is a functionally flexible and rapidly reconfigurable CMOS RFIC compactly implemented on silicon with the potential to achieve the cost, size and power targets in mass-market applications. While the focus of this thesis is RF signal finding and reception in frequency, the CS-based RFIC design approach presented here is applicable to a wide range of other applications like direction-of-arrival and range finding.
We begin by developing a signal-model driven approach for optimizing the performance of CS RF frontends (RFFEs). We consider sparse multiband signals with supports contained within a frequency span extending from fMIN to fMAX. The resulting quadrature analog-to-information converter (QAIC) is a flexible-bandwidth, blind sub-Nyquist sampling architecture optimized for energy consumption and sensitivity performance. The QAIC addresses key drawbacks of earlier CS RFFE architectures like the modulated wideband converter (MWC) that implement frequency spans extending from 0 to fMAX. While these earlier architectures, a direct implementation of CS signal processing theory, have several beneficial properties, the true cost of their proposed analog frontend significantly diminishes the sensitivity performance and energy savings that CS methods have the potential to deliver. They use periodic pseudo-random bit sequence (PRBS) generators where the clock frequency fPRBS scales up with the maximum signal frequency fMAX. In contrast, fPRBS in the QAIC RFFE scales up with the instantaneous bandwidth IBW, where IBW = ( fMAX − fMIN ). This results in significant performance advantages in terms of energy consumption and sensitivity performance. The QAIC uncouples fPRBS from fMAX by performing wideband quadrature downconversion ahead of analog mixing with PRBSs at an intermediate frequency (IF). However, the dual heterodyne architecture of the QAIC suffers from spurious responses at IF caused by gain and phase imbalance in its wideband downconverter.
We then show how the direct RF-to-information converter (DRF2IC) compactly adds CS wideband detection to a direct conversion frequency-translational noise-cancelling (FTNC) receiver by introducing pseudo-random modulation of the local oscillator (LO) signals and by consolidating multiple CS measurements into one hardware branch. The DRF2IC inherits benefits of the FTNC receiver in signal reception mode. In CS wideband detection mode, the DRF2IC inherits key advantages from both the earlier lowpass CS architectures and the QAIC while avoiding the drawbacks of both. It uncouples fPRBS from fMAX in contrast with the MWC. In contrast with the QAIC, the DRF2IC employs a direct conversion RF chain with narrow bandwidth analog components at baseband thereby avoiding frequency-dependent gain and phase imbalance. The DRF2IC chip occupies 0.56mm2 area in 65nm CMOS. In reception mode, it consumes 46.5mW from 1.15V and delivers 40MHz RF bandwidth, 41.5dB conversion gain, 3.6dB noise figure (NF) and -2dBm blocker 1dB compression point (B1dB). In CS wideband detection mode, 66dB operational dynamic range, 40dB instantaneous dynamic range and 1.43GHz instantaneous bandwidth are demonstrated and 6 interferers each 10MHz wide scattered over a 1.27GHz span are detected in 1.2us consuming 58.5mW.
This item is currently under embargo. It will be available starting 2021-08-21.
More About This Work
- Academic Units
- Electrical Engineering
- Thesis Advisors
- Kinget, Peter R.
- Wright, John N.
- Ph.D., Columbia University
- Published Here
- October 7, 2019