Rabia Tugce Yazicigil

Theory and Design of a Quadrature Analog-to-Information Converter for Energy-Efficient Wideband Spectrum Sensing

A flexible bandwidth, blind sub-Nyquist sampling approach referred to as the quadrature analog-to-information converter (QAIC) is proposed. The QAIC relaxes the analog front-end bandwidth requirements at the cost of some added complexity compared to the modulated wideband converter (MWC) for an overall improvement in sensitivity and energy consumption. An approach for detailed frequency domain analysis of the proposed system with linear impairments is developed. A process for selecting QAIC parameter values is illustrated through examples. The benefits of the QAIC are highlighted with cognitive radio use cases where a wide range of spectrum is observed at various resolution bandwidth settings. We demonstrate that the energy consumption of the QAIC is potentially two orders of magnitude lower than the swept-tuned spectrum analyzer (STSA) and an order of magnitude lower than the MWC. We also demonstrate that the QAIC significantly improves upon the sensitivity performance delivered by the MWC.

Wideband Rapid Interferer Detector Exploiting Compressed Sampling With a Quadrature Analog-to-Information Converter

We introduce a rapid interferer detector that uses compressed sampling (CS) with a quadrature analog-to-information converter (QAIC). By exploiting bandpass CS, a blind sub-Nyquist sampling approach, the QAIC offers an energy efficient and rapid interferer detection over a wide instantaneous bandwidth. The QAIC front end is implemented in 65 nm CMOS in 0.43 mm 2 and consumes 81 mW from a 1.1 V supply. It senses a frequency span of 1 GHz ranging from 2.7 to 3.7 GHz (PCAST Band) with a resolution bandwidth of 20 MHz in 4.4 μs, 50 times faster than traditional sweeping spectrum scanners. Rapid interferer detector with the bandpass QAIC is two orders of magnitude more energy efficient than traditional Nyquist-rate architectures and one order of magnitude more energy efficient than existing low-pass CS methods. Thanks to CS, the aggregate sampling rate of the QAIC interferer detector is compressed by 6.3 × compared to traditional Nyquist-rate architectures for the same instantaneous bandwidth.

A compressed-sampling time-segmented quadrature analog-to-information converter for wideband rapid detection of up to 6 interferers with adaptive thresholding

A rapid interferer-detector for cognitive radio systems is presented that uses a compressed-sampling time-segmented quadrature analog-to-information converter (TS-QAIC). The TS-QAIC introduces system scalability through adaptive thresholding and time segmentation, while limiting silicon cost and complexity. The TS-QAIC can detect 6 interferers in 2.7-3.7GHz in 10.4μs with 8 physical I/Q branches. The TS-QAIC chip implemented in 65nm CMOS on 0.517mm2 (active area) consumes 81.2mW from 1.2V.

RF circuit and system innovations for a new generation of wireless terminals

Next-generation (Next-G) wireless terminals need to sense their ambient and adapt to the diverse deployment scenario requirements on the fly while leveraging technology scaling. Several key circuit and system innovations are required to make the realization of this vision possible. We discuss how compressed sampling can be exploited to design a rapid, GHz-wide and energy-efficient interferer detector using a quadrature analog-to-information converter. A family of field-programmable receiver front ends demonstrating two linearity enhancement techniques including interferer-reflecting loops and hybrid Class-AB-C low noise transconductors is discussed. The technology scalable and out-of-band blocker robust switched-capacitor RF front end is then presented.

How to Make Analog-to-Information Converters Work in Dynamic Spectrum Environments With Changing Sparsity Conditions

Compressed sensing (CS) analog to information converters (AICs) offer key benefits for signal reception or detection when the input signal is sparse. So far AICs have been demonstrated in environments with controlled input signal conditions and with fixed sparsity levels. This paper investigates how to make AICs effectively operate in dynamic environments with changing signal conditions and thus changing sparsity levels. We focus on RF spectrum scanning, where signals or interferers need to be detected across a wide dynamic RF spectrum, but the presented concepts are widely applicable for low-pass and band-pass CS AICs. The number of measurements and hence the number of branches required in a CS RF front end scales with the sparsity level, i.e. the number of signals that need to be detected. In practice this leads to excessively large silicon area for more than a few signals (e.g., six). We introduce the time-segmented quadrature analog-to-information converter (TS-QAIC), a scalable architecture for signal detection in dynamically changing spectrum environments. While our TS-QAIC prototype implements a fixed number of hardware branches, we experimentally demonstrate adaptive thresholding and adaptive time segmentation to adjust its signal detection capability to the sparsity level of the input signal.

Single-BAW multi-channel transmitter with low power and fast start-up time

This paper presents a multi-channel transmitter (TX) architecture that uses only a single bulk acoustic wave (BAW) resonator while covering 88 MHz of bandwidth. The proposed architecture overcomes the limited tuning range of a single BAW resonator by combining the BAW tuning range with a programmable integer-N frequency division and RF single-sideband (SSB) mixing approach. The single-BAW multi-channel TX achieves 88 MHz-wide frequency coverage with 1 MHz channels. It operates in the 2.4 GHz ISM band and the full system is demonstrated with 0 dBm output power and a fast system startup time of 2.3 μs enabled by the BAW resonator. It is implemented in 65 nm CMOS technology in a 2 mm × 2 mm area and consumes 6.4 mW from a 1.1 V supply.

Band-pass compressive sampling as an enabling technology for rapid wideband RF spectrum sensing

Spectrum is the lifeblood of the future wireless networks and the data storm driven by the emerging technologies like Internet of Things, video over wireless will lead to a pressing artificial spectrum scarcity. Future smart terminals will need to quickly assess the spectrum usage and opportunistically use the available spectrum to overcome this challenge. They require energy-efficient spectrum scanning capabilities. We developed quadrature analog-to-information converters (QAIC), an energy-efficient compressed-sampling (CS) hardware architecture to process band-pass signals especially at RF frequencies [1]. The band-pass CS QAIC system offers a novel approach to attack the search for the quick detection of interferers in a wideband spectrum in an energy-efficient way. We further demonstrated time segmentation and adaptive thresholding with a time-segmented QAIC (TS-QAIC) to extend the interferer detection capabilities without any additional silicon cost and area [2], [3]. Such detectors are key cornerstones for future multi-tiered shared spectrum access solutions with dynamic spectrum sensing.