Compressive Sampling as an Enabling Solution for Energy-Efficient and Rapid Wideband RF Spectrum Sensing in Emerging Cognitive Radio Systems

Wireless systems have become an essential part of every sector of the national and global economy. In addition to existing commercial systems including GPS, mobile cellular, and WiFi communications, emerging systems like video over wireless, the Internet of Things, and machine-to-machine communications are expected to increase mobile wireless data traffic by several orders of magnitude over the coming decades, while natural resources like energy and radio spectrum remain scarce. The projected growth of the number of connected nodes into the trillions in the near term and increasing user demand for instantaneous, over-the-air access to large volumes of content will require a 1000-fold increase in network wireless data capacity by 2020. Spectrum is the lifeblood of these future wireless networks and the ’data storm’ driven by emerging technologies will lead to a pressing ’artificial’ spectrum scarcity. Cognitive radio is a paradigm proposed to overcome the existing challenge of underutilized spectrum. Emerging cognitive radio systems employing multi-tiered, shared-spectrum access are expected to deliver superior spectrum efficiency over existing scheduled-access systems; they have several device categories (3 or more tiers) with different access privileges. We focus on lower tiered ’smart’ devices that evaluate the spectrum dynamically and opportunistically use the underutilized spectrum. These ’smart’ devices require spectrum sensing for incumbent detection and interferer avoidance. Incumbent detection will rely on database lookup or narrowband high-sensitivity sensing. Integrated interferer detectors, on the other hand, need to be fast, wideband, and energy efficient, while requiring only moderate sensitivity. These future 'smart' devices operating in small cell environments will need to rapidly (in 10s of μs) detect a few (e.g. 3 to 6) strong interferers within roughly a 1GHz span and accordingly reconfigure their hardware resources or request adjustments to their wireless connection consisting of primary and secondary links in licensed and unlicensed spectrum. Compressive sampling (CS), an evolutionary sensing/sampling paradigm that changes the perception of sampling, has been extensively used for image reconstruction. It has been shown that a single pixel camera that exploits CS has the ability to obtain an image with a single detection element, while measuring the image fewer times than the number of pixels with the prior assumption of sparsity. We exploited CS in the presented works to take a ’snapshot’ of the spectrum with low energy consumption and high frequency resolutions. Compressive sampling is applied to break the fixed trade-off between scan time, resolution bandwidth, hardware complexity, and energy consumption. This contrasts with traditional spectrum scanning solutions, which have constant energy consumption in all architectures to first order and a fixed trade-off between scan time and resolution bandwidth. Compressive sampling enables energy-efficient, rapid, and wideband spectrum sensing with high frequency resolutions at the expense of degraded instantaneous dynamic range due to the noise folding. We have developed a quadrature analog-to-information converter (QAIC), a novel CS rapid spectrum sensing technique for band-pass signals. Our first wideband, energy-efficient, and rapid interferer detector end-to-end system with a QAIC senses a wideband 1GHz span with a 20MHz resolution bandwidth and successfully detects up to 3 interferers in 4.4μs. The QAIC offers 50x faster scan time compared to traditional sweeping spectrum scanners and 6.3x the compressed aggregate sampling rate of traditional concurrent Nyquist-rate approaches. The QAIC is estimated to be two orders of magnitude more energy efficient than traditional spectrum scanners/sensors and one order of magnitude more energy efficient than existing low-pass CS spectrum sensors. We implemented a CS time-segmented quadrature analog-to-information converter (TS-QAIC) that extends the physical hardware through time segmentation (e.g. 8 physical I/Q branches to 16 I/Q through time segmentation) and employs adaptive thresholding to react to the signal conditions without additional silicon cost and complexity. The TS-QAIC rapidly detects up to 6 interferers in the PCAST spectrum between 2.7 and 3.7GHz with a 10.4μs sensing time for a 20MHz RBW with only 8 physical I/Q branches while consuming 81.2mW from a 1.2V supply. The presented rapid sensing approaches enable system scaling in multiple dimensions such as ADC bits, the number of samples, and the number of branches to meet user performance goals (e.g. the number of detectable interferers, energy consumption, sensitivity and scan time). We envision that compressive sampling opens promising avenues towards energy-efficient and rapid sensing architectures for future cognitive radio systems utilizing multi-tiered, shared spectrum access

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

Design of Power-Efficient Optical Transceivers and Design of High-Linearity Wireless Wideband Receivers

The combination of silicon photonics and advanced heterogeneous integration is promising for next-generation disaggregated data centers that demand large scale, high throughput, and low power. In this dissertation, we discuss the design and theory of power-efficient optical transceivers with System-in-Package (SiP) 2.5D integration. Combining prior arts and proposed circuit techniques, a receiver chip and a transmitter chip including two 10 Gb/s data channels and one 2.5 GHz clocking channel are designed and implemented in 28 nm CMOS technology. An innovative transimpedance amplifier (TIA) and a single-ended to differential (S2D) converter are proposed and analyzed for a low-voltage high-sensitivity receiver; a four-to-one serializer, programmable output drivers, AC coupling units, and custom pads are implemented in a low-power transmitter; an improved quadrature locked loop (QLL) is employed to generate accurate quadrature clocks. In addition, we present an analysis for inverter-based shunt-feedback TIA to explicitly depict the trade-off among sensitivity, data rate, and power consumption. At last, the research on CDR-based​ clocking schemes for optical links is also discussed. We introduce prior arts and propose a power-efficient clocking scheme based on an injection-locked phase rotator. Next, we analyze injection-locked ring oscillators (ILROs) that have been widely used for quadrature clock generators (QCGs) in multi-lane optical or wireline transceivers due to their low power, low area, and technology scalability. The asymmetrical or partial injection locking from 2 phases to 4 phases results in imbalances in amplitude and phase. We propose a modified frequency-domain analysis to provide intuitive insight into the performance design trade-offs. The analysis is validated by comparing analytical predictions with simulations for an ILRO-based QCG in 28 nm CMOS technology. This dissertation also discusses the design of high-linearity wireless wideband receivers. An out-of-band (OB) IM3 cancellation technique is proposed and analyzed. By exploiting a baseband auxiliary path (AP) with a high-pass feature, the in-band (IB) desired signal and out-of-band interferers are split. OB third-order intermodulation products (IM3) are reconstructed in the AP and cancelled in the baseband (BB). A 0.5-2.5 GHz frequency-translational noise-cancelling (FTNC) receiver is implemented in 65nm CMOS to demonstrate the proposed approach. It consumes 36 mW without cancellation at 1 GHz LO frequency and 1.2 V supply, and it achieves 8.8 MHz baseband bandwidth, 40dB gain, 3.3dB NF, 5dBm OB IIP3, and −6.5dBm OB B1dB. After IM3 cancellation, the effective OB-IIP3 increases to 32.5 dBm with an extra 34 mW for narrow-band interferers (two tones). For wideband interferers, 18.8 dB cancellation is demonstrated over 10 MHz with two −15 dBm modulated interferers. The local oscillator (LO) leakage is −92 dBm and −88 dB at 1 GHz and 2 GHz LO respectively. In summary, this technique achieves both high OB linearity and good LO isolation

Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access

Besides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis, 1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information recovery than that achieved by existing approaches. 2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. 3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead. 4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users

The Global Navigation System Scope (GNSScope): a toolbox for the end-to-end modelling simulation and analysis of GNSS

The thesis provides a detailed overview of the work carried out by the author over the course of the research for the award of the degree of Doctor of Philosophy at the University of Westminster, and the performance results of the novel techniques introduced into the literature. The outcome of the work is collectively referred to as the Global Navigation System Scope (GNSScope) Toolbox, offering a complete, fully reconfigurable platform for the end-to-end modeling, simulation and analysis of satellite navigation signals and systems, covering the signal acquisition, tracking, and range processing operations that take place in a generic Global Navigation Satellite System (GNSS) receiver, accompanied by a Graphical User Interface (GUI) providing access to all the techniques available in the toolbox. Designed and implemented entirely in the MATLAB mathematical programming environment using Software Defined Radio (SDR) receiver techniques, the toolbox offers a novel new acquisition algorithm capable of handling all Phase-Shift Keying (PSK) type modulations used on all frequency bands in currently available satellite navigation signals, including all sub-classes of the Binary Offset Carrier (BOC) modulated signals. In order to be able to process all these signals identified by the acquisition search, a novel tracking algorithm was also designed and implemented into the toolbox to track and decode all acquired satellite signals, including those currently intended to be used in future navigation systems, such as the Galileo test signals transmitted by the GIOVE satellites orbiting the Earth. In addition to the developed receiver toolbox, three novel algorithms were also designed to handle weak signals, multipath, and multiple access interference in GNSScope. The Mirrored Channel Mitigation Technique, based on the successive and parallel interference cancellation techniques, reduces the hardware complexity of the interference mitigation process by utilizing the local code and carrier replicas generated in the tracking channels, resulting in a reduction in hardware resources proportional to the number of received strong signals. The Trigonometric Interference Cancellation Technique, used in cross-correlation interference mitigation, exploits the underlying mathematical expressions to simplify the interference removal process, resulting in reduced complexity and execution times by reducing the number of operations by 25% per tracking channel. The Split Chip Summation Technique, based on the binary valued signal modulation compression technique, enhances the amount of information captured from compressing the signal to reveal specific filtering effects on the positive and negative polarity chips of the spreading code. Simulation case studies generated entirely using the GNSScope toolbox will be used throughout the thesis to demonstrate the effectiveness of the novel techniques developed over the course of the research, and the results will be compared to those obtained from other techniques reported in the literature

Space-time-frequency methods for interference-limited communication systems

textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin

Design of Fully-Integrated High-Resolution Radars in CMOS and BiCMOS Technologies

The RADAR, acronym that stands for RAdio Detection And ranging, is a device that uses electromagnetic waves to detect the presence and the distance of an illuminated target. The idea of such a system was presented in the early 1900s to determine the presence of ships. Later on, with the approach of World War II, the radar gained the interest of the army who decided to use it for defense purposes, in order to detect the presence, the distance and the speed of ships, planes and even tanks. Nowadays, the use of similar systems is extended outside the military area. Common applications span from weather surveillance to Earth composition mapping and from flight control to vehicle speed monitoring. Moreover, the introduction of new ultrawideband (UWB) technologies makes it possible to perform radar imaging which can be successfully used in the automotive or medical field. The existence of a plenty of known applications is the reason behind the choice of the topic of this thesis, which is the design of fully-integrated high-resolution radars. The first part of this work gives a brief introduction on high resolution radars and describes its working principle in a mathematical way. Then it gives a comparison between the existing radar types and motivates the choice of an integrated solution instead of a discrete one. The second part concerns the analysis and design of two CMOS high-resolution radar prototypes tailored for the early detection of the breast cancer. This part begins with an explanation of the motivations behind this project. Then it gives a thorough system analysis which indicates the best radar architecture in presence of impairments and dictates all the electrical system specifications. Afterwards, it describes in depth each block of the transceivers with particular emphasis on the local oscillator (LO) generation system which is the most critical block of the designs. Finally, the last section of this part presents the measurement results. In particular, it shows that the designed radar operates over 3 octaves from 2 to 16GHz, has a conversion gain of 36dB, a flicker-noise-corner of 30Hz and a dynamic range of 107dB. These characteristics turn into a resolution of 3mm inside the body, more than enough to detect even the smallest tumor. The third and last part of this thesis focuses on the analysis and design of some important building blocks for phased-array radars, including phase shifter (PHS), true time delay (TTD) and power combiner. This part begins with an exhaustive introduction on phased array systems followed by a detailed description of each proposed lumped-element block. The main features of each block is the very low insertion loss, the wideband characteristic and the low area consumption. Finally, the major effects of circuit parasitics are described followed by simulation and measurement results