skip to main content

Title: Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion
Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of ℓ₁-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
IEEE Transactions on Circuits and Systems I: Regular Papers
Page Range / eLocation ID:
1 to 14
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Feature extraction from wideband radio-frequency (RF) signals, such as spectral activity, interferer energy and type, or direction- of-arrival, finds use in a growing number of applications. Compressive sensing (CS)-based analog-to-information (A2I) converters enable the design of inexpensive and energy-efficient wideband RF sensing solutions for such applications. However, most A2I architectures suffer from a variety of real-world impairments. We propose a novel A2I architecture, referred to as non-uniform wavelet bandpass sampling (NUWBS). Our architecture extracts a carefully-tuned subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of most existing A2I converters. We use simulations to show that NUWBS approaches the performance limits of l1-norm-based sparse signal recovery. 
    more » « less
  2. Abstract

    Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

    more » « less
  3. Waleed Khalil (Ed.)
    The increasing performance demanded by emerging wireless communication standards motivates the development of various techniques devoted to improving the efficiency of power amplifiers (PA) because this is one of the most power-demanding blocks in RF transceivers. Power-amplifier efficiency is proportional to the ratio of the average voltage delivered by the PA to the voltage level of the PA's power supply. Efficiency is affected by the peak-to-average ratio of the transmitted signal. The envelope tracking modulator maximizes this ratio, correlating the PA's power supply with the envelope of its output signal. Efficient modulators must satisfy certain critical conditions: i) it must be very agile to track the amplitude variations of PA's output voltage; ii) it must reduce the timing mismatch between the PA modulator's supply and PA output waveform envelope to optimize power efficiency and avoid PA saturation, and iii) the envelope tracking modulator must be highly power efficient. This paper reviews several relevant envelope tracking techniques. Hybrid modulators consisting of switching regulators and linear amplifiers have become mainstream envelope tracking systems for wideband applications, in which linear amplifiers complement the functionality of highly efficient but narrow bandwidth switching modulators. Replacements for linear amplifiers include a combination of power-efficient ADC and DACs that provide very agile feedback, increasing the system's slew rate, which allows the modulator to track faster envelope signals. Multi-level switching is another relevant approach utilizing multiple switching voltages to reduce current ripples and enable the use of wider bandwidth switching regulators with high power efficiency. The use of multiple inductors is another interesting approach. Multi-phase switching techniques utilize multiple switching stages in a time-interleaved manner to extend the switching modulator's bandwidth. A slow buck converter can be combined with a fast buck converter and optimized for different switching frequencies; this architecture covers the signal envelope's low- and high-frequency components. The approaches mentioned use switching modulators with analog feedback controllers (Pulse-width modulation [PWM] or hysteretic). However, an alternative approach is prediction-based digital feedforward control. This tutorial discusses all of these approaches. 
    more » « less
  4. Multi-channel data acquisition of bio-signals is a promising technology that is being used in many fields these days. Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as an electrocardiogram (ECG) and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increase, data volume is increased resulting in impermissible transmitting power. This paper presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. The power consumed by the bio-signal amplifier block is 2.35 μW. The SAR-ADC block that is designed to digitize the amplified signal consumes 277 μW of power and the power consumption value of the pseudo-random bit sequence generator (PRBS) is 344.2μW. The sampling rate of PRBS block is 611.76 Kbps. We have considered collecting neural data from the 32 channels, and achieved an 8.5X compression rate. The low power consumption per channel confirms the importance of the proposed approach for multiple channel high-density neural interfaces. 
    more » « less
  5. While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality.This study proposes PROS, an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-off between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) efficiently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing efficiency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath.We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high fidelity signal. It boosts the power efficiency of the wearable device by more than 1200\% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals. 
    more » « less