Sparsity-Aware Low-Power ADC Architecture with Advanced Reconstruction Algorithms

Compressive sensing (CS) technique enables a universal sub-Nyquist sampling of sparse and compressible signals, while still guaranteeing the reliable signal recovery. Its potential lies in the reduced analog-to-digital conversion rate in sampling broadband and/or multi-channel sparse signals, where conventional Nyquist-rate sampling are either technology impossible or extremely hardware costly. Nevertheless, there are many challenges in the CS hardware design. In coherent sampling, state-of-the-art mixed-signal CS front-ends, such as random demodulator and modulated wideband converter, suffer from high power and nonlinear hardware. In signal recovery, state-of-the-art CS reconstruction methods have tractable computational complexity and probabilistically guaranteed performance. However, they are still high cost (basis pursuit) or noise sensitive (matching pursuit). In this dissertation, we propose an asynchronous compressive sensing (ACS) front-end and advanced signal reconstruction algorithms to address these challenges. The ACS front-end consists of a continuous-time ternary encoding (CT-TE) scheme which converts signal amplitude variations into high-rate ternary timing signal, and a digital random sampler (DRS) which captures the ternary timing signal at sub-Nyquist rate. The CT-TE employs asynchronous sampling mechanism for pulsed-like input and has signal-dependent conversion rate. The DRS has low power, ease of massive integration, and excellent linearity in comparison to state-of-the-art mixed-signal CS front-ends. We propose two reconstruction algorithms. One is group-based total variation, which exploits piecewise-constant characteristics and achieves better mean squared error and faster convergence rate than the conventional TV scheme with moderate noise. The second algorithm is split-projection least squares (SPLS), which relies on a series of low-complexity and independent l2-norm problems with the prior on ternary-valued signal. The SPLS scheme has good noise robustness, low-cost signal reconstruction and facilitates a parallel hardware for real-time signal recovery. In application study, we propose multi-channel filter banks ACS front-end for the interference-robust radar. The proposed receiver performs reliable target detection with nearly 8-fold data compression than Nyquist-rate sampling in the presence of -50dBm wireless interference. We also propose an asynchronous compressed beamformer (ACB) for low-power portable diagnostic ultrasound. The proposed ACB achieves 9-fold data volume compression and only 4.4% contrast-to-noise ratio loss on the imaging results when compared with the Nyquist-rate ADCs

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

Self-powered Time-Keeping and Time-of-Occurrence Sensing

Self-powered and passive Internet-of-Things (IoT) devices (e.g. RFID tags, financial assets, wireless sensors and surface-mount devices) have been widely deployed in our everyday and industrial applications. While diverse functionalities have been implemented in passive systems, the lack of a reference clock limits the design space of such devices used for applications such as time-stamping sensing, recording and dynamic authentication. Self-powered time-keeping in passive systems has been challenging because they do not have access to continuous power sources. While energy transducers can harvest power from ambient environment, the intermittent power cannot support continuous operation for reference clocks. The thesis of this dissertation is to implement self-powered time-keeping devices on standard CMOS processes. In this dissertation, a novel device that combines the physics of quantum tunneling and floating-gate (FG) structures is proposed for self-powered time-keeping in CMOS process. The proposed device is based on thermally assisted Fowler-Nordheim (FN) tunneling process across high-quality oxide layer to discharge the floating-gate node, therefore resulting in a time-dependent FG potential. The device was fully characterized in this dissertation, and it does not require external powering during runtime, making it feasible for passive devices and systems. Dynamic signature based on the synchronization and desynchronization behavior of the FN timer is proposed for authentication of IoT devices. The self-compensating physics ensure that when distributed timers are subjected to identical environment variances that are common-mode noise, they can maintain synchronization with respect to each other. On the contrary, different environment conditions will desynchronize the timers creating unique signatures. The signatures could be used to differentiate between products that belong to different supply-chains or products that were subjected to malicious tampering. SecureID type dynamic authentication protocols based on the signature generated by the FN timers are proposed and they are proven to be robust to most attacks. The protocols are further analyzed to be lightweight enough for passive devices whose computational sources are limited. The device could also be applied for self-powered sensing of time-of-occurrence. The prototype was verified by integrating the device with a self-powered mechanical sensor to sense and record time-of-occurrence of mechanical events. The system-on-chip design uses the timer output to modulate a linear injector to stamp the time information into the sensing results. Time-of-occurrence can be reconstructed by training the mathematical model and then applying that to the test data. The design was verified to have a high reconstruction accuracy

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC

Ultra-Wideband Relay Communication Systems

Impulse-radio ultra-wide-band (IR-UWB) signaling is a promising technique for high-speed, short-range relay communications networks. Depending on how the relay node retransmits the signal, there are two main relay schemes: conventional one-directional (one-way) relay model, and bi-directional (two-way) relay model. In bi-directional relay communications, wireless network coding (WNC), also called physical-layer network coding (PNC), could be applied to overcome the spectral efficiency limitation of the conventional one-way relay. In the first part of this work, we propose asynchronous, differential, and bidirectional decode and forward (ADBDF) and asynchronous, differential, and bidirectional denoise and forward (ADBDNF) UWB relay methods, where the relay node (RN) does not need to be synchronized with the end nodes (ENs). The proposed schemes are attractive for networks in which stringent/complicated synchronization between the RN and the ENs may not be feasible. The second part of this work focuses on UWB channel classification. We propose a 2-dimensional (2-D) LOS/NLOS classification scheme that uses skewness of the channel impulse/pulse response. The proposed channel classification decreases the complexity of existing channel classification methods and can be used in a variety of areas such as localization, relay communications, and cooperative communications. The final part of this work deals with compressive sensing (CS) algorithms that employ sub-Nyquist sampling for UWB communications. We develop coarse graining (CG) for the proposed CS sub-Nyquist sampling technique, which leads to: (1) reduced sampling rate at the receiver, and hence reduced use of analog-to-digital converters (ADCs) resources; and (2) low-complexity channel estimation