Performance evaluation of 5G millimeter-wave cellular access networks using a capacity-based network deployment tool

The next fifth generation (5G) of wireless communication networks comes with a set of new features to satisfy the demand of data-intensive applications: millimeter-wave frequencies, massive antenna arrays, beamforming, dense cells, and so forth. In this paper, we investigate the use of beamforming techniques through various architectures and evaluate the performance of 5G wireless access networks, using a capacity-based network deployment tool. This tool is proposed and applied to a realistic area in Ghent, Belgium, to simulate realistic 5G networks that respond to the instantaneous bit rate required by the active users. The results show that, with beamforming, 5G networks require almost 15% more base stations and 4 times less power to provide more capacity to the users and the same coverage performances, in comparison with the 4G reference network. Moreover, they are 3 times more energy efficient than the 4G network and the hybrid beamforming architecture appears to be a suitable architecture for beamforming to be considered when designing a 5G cellular network

Ultra-Low-Power Configurable Analog Signal Processor for Wireless Sensors

The demand for on-chip low-power Complementary Metal Oxide Semiconductor (CMOS) analog signal processing has significantly increased in recent years. Digital signal processors continue to shrink in size as transistors half in size every two years. However, digital signal processors (DSP\u27s) notoriously use more power than analog signal processors (APS\u27s). This thesis presents a configurable analog signal processor (CASP) used for wireless sensors. This CASP contains a multitude of processing blocks include the following: low pass filter (LPF), high pass filter (HPF) integrator, differentiator, operational transconductance amplifier (OTA), rectifier with absolute value functionality, and multiplier. Each block uses current-mode processing and operates in the sub-threshold region of operation. Current-mode processing allows for noise reduction, lower power consumption, and better dynamic range. Each block contains configurable current sources and capacitor banks for maximum adaptability. The blocks were designed, simulated, and fabricated in Cadence using IBM\u27s 130nm CMOS process. The processing blocks were combined into a four by three array and connected using specially designed interconnect fabric. A test structure including the LPF, HPF, and multiplier was also constructed for characterization purposes. The main goals for this project are frequency compression and creating a non-linear energy operator for neural spike detection. The test results for the low-pass filter, integrator, and frequency divider reflected the simulated values. The other blocks didn\u27t perform as well as in simulation. The interconnect fabric ties all the blocks together and achieved maximum configurability with negligible attenuation. In simulation, frequency compression was achieved with 30u[micro]W of power from a 1V supply rail

Modeling and Analysis of Power Processing Systems

The feasibility of formulating a methodology for the modeling and analysis of aerospace electrical power processing systems is investigated. It is shown that a digital computer may be used in an interactive mode for the design, modeling, analysis, and comparison of power processing systems

Advanced CMOS Integrated Circuit Design and Application

The recent development of various application systems and platforms, such as 5G, B5G, 6G, and IoT, is based on the advancement of CMOS integrated circuit (IC) technology that enables them to implement high-performance chipsets. In addition to development in the traditional fields of analog and digital integrated circuits, the development of CMOS IC design and application in high-power and high-frequency operations, which was previously thought to be possible only with compound semiconductor technology, is a core technology that drives rapid industrial development. This book aims to highlight advances in all aspects of CMOS integrated circuit design and applications without discriminating between different operating frequencies, output powers, and the analog/digital domains. Specific topics in the book include: Next-generation CMOS circuit design and application; CMOS RF/microwave/millimeter-wave/terahertz-wave integrated circuits and systems; CMOS integrated circuits specially used for wireless or wired systems and applications such as converters, sensors, interfaces, frequency synthesizers/generators/rectifiers, and so on; Algorithm and signal-processing methods to improve the performance of CMOS circuits and systems

Integrated Electronics for Wireless Imaging Microsystems with CMUT Arrays

Integration of transducer arrays with interface electronics in the form of single-chip CMUT-on-CMOS has emerged into the field of medical ultrasound imaging and is transforming this field. It has already been used in several commercial products such as handheld full-body imagers and it is being implemented by commercial and academic groups for Intravascular Ultrasound and Intracardiac Echocardiography. However, large attenuation of ultrasonic waves transmitted through the skull has prevented ultrasound imaging of the brain. This research is a prime step toward implantable wireless microsystems that use ultrasound to image the brain by bypassing the skull. These microsystems offer autonomous scanning (beam steering and focusing) of the brain and transferring data out of the brain for further processing and image reconstruction. The objective of the presented research is to develop building blocks of an integrated electronics architecture for CMUT based wireless ultrasound imaging systems while providing a fundamental study on interfacing CMUT arrays with their associated integrated electronics in terms of electrical power transfer and acoustic reflection which would potentially lead to more efficient and high-performance systems. A fully wireless architecture for ultrasound imaging is demonstrated for the first time. An on-chip programmable transmit (TX) beamformer enables phased array focusing and steering of ultrasound waves in the transmit mode while its on-chip bandpass noise shaping digitizer followed by an ultra-wideband (UWB) uplink transmitter minimizes the effect of path loss on the transmitted image data out of the brain. A single-chip application-specific integrated circuit (ASIC) is de- signed to realize the wireless architecture and interface with array elements, each of which includes a transceiver (TRX) front-end with a high-voltage (HV) pulser, a high-voltage T/R switch, and a low-noise amplifier (LNA). Novel design techniques are implemented in the system to enhance the performance of its building blocks. Apart from imaging capability, the implantable wireless microsystems can include a pressure sensing readout to measure intracranial pressure. To do so, a power-efficient readout for pressure sensing is presented. It uses pseudo-pseudo differential readout topology to cut down the static power consumption of the sensor for further power savings in wireless microsystems. In addition, the effect of matching and electrical termination on CMUT array elements is explored leading to new interface structures to improve bandwidth and sensitivity of CMUT arrays in different operation regions. Comprehensive analysis, modeling, and simulation methodologies are presented for further investigation.Ph.D

A 16-Channel Neural Recording System-on-Chip With CHT Feature Extraction Processor in 65-nm CMOS

Next-generation invasive neural interfaces require fully implantable wireless systems that can record from a large number of channels simultaneously. However, transferring the recorded data from the implant to an external receiver emerges as a significant challenge due to the high throughput. To address this challenge, this article presents a neural recording system-on-chip that achieves high resource and wireless bandwidth efficiency by employing on-chip feature extraction. Energy-area-efficient 10-bit 20-kS/s front end amplifies and digitizes the neural signals within the local field potential (LFP) and action potential (AP) bands. The raw data from each channel are decomposed into spectral features using a compressed Hadamard transform (CHT) processor. The selection of the features to be computed is tailored through a machine learning algorithm such that the overall data rate is reduced by 80% without compromising classification performance. Moreover, the CHT feature extractor allows waveform reconstruction on the receiver side for monitoring or additional post-processing. The proposed approach was validated through in vivo and off-line experiments. The prototype fabricated in 65-nm CMOS also includes wireless power and data receiver blocks to demonstrate the energy and area efficiency of the complete system. The overall signal chain consumes 2.6 μW and occupies 0.021 mm² per channel, pointing toward its feasibility for 1000-channel single-die neural recording systems

Development of a Myoelectric Detection Circuit Platform for Computer Interface Applications

Personal computers and portable electronics continue to rapidly advance and integrate into our lives as tools that facilitate efficient communication and interaction with the outside world. Now with a multitude of different devices available, personal computers are accessible to a wider audience than ever before. To continue to expand and reach new users, novel user interface technologies have been developed, such as touch input and gyroscopic motion, in which enhanced control fidelity can be achieved. For users with limited-to-no use of their hands, or for those who seek additional means to intuitively use and command a computer, novel sensory systems can be employed that interpret the natural electric signals produced by the human body as command inputs. One of these novel sensor systems is the myoelectric detection circuit, which can measure electromyographic (EMG) signals produced by contracting muscles through specialized electrodes, and convert the signals into a usable form through an analog circuit. With the goal of making a general-purpose myoelectric detection circuit platform for computer interface applications, several electrical circuit designs were iterated using OrCAD software, manufactured using PCB fabrication techniques, and tested with electrical measurement equipment and in a computer simulation. The analog circuit design culminated in a 1.35” x 0.8” manufactured analog myoelectric detection circuit unit that successfully converts a measured EMG input signal from surface skin electrodes to a clean and usable 0-5 V DC output that seamlessly interfaces with an Arduino Leonardo microcontroller for further signal processing and logic operations. Multiple input channels were combined with a microcontroller to create an EMG interface device that was used to interface with a PC, where simulated mouse cursor movement was controlled through the voluntary EMG signals provided by a user. Functional testing of the interface device was performed, which showed a long battery life of 44.6 hours, and effectiveness in using a PC to type with an on-screen keyboard

The 30-cm ion thruster power processor

A power processor unit for powering and controlling the 30 cm Mercury Electron-Bombardment Ion Thruster was designed, fabricated, and tested. The unit uses a unique and highly efficient transistor bridge inverter power stage in its implementation. The system operated from a 200 to 400 V dc input power bus, provides 12 independently controllable and closely regulated dc power outputs, and has an overall power conditioning capacity of 3.5 kW. Protective circuitry was incorporated as an integral part of the design to assure failure-free operation during transient and steady-state load faults. The implemented unit demonstrated an electrical efficiency between 91.5 and 91.9 at its nominal rated load over the 200 to 400 V dc input bus range

Integrated circuit & system design for concurrent amperometric and potentiometric wireless electrochemical sensing

Complementary Metal-Oxide-Semiconductor (CMOS) biosensor platforms have steadily grown in healthcare and commerial applications. This technology has shown potential in the field of commercial wearable technology, where CMOS sensors aid the development of miniaturised sensors for an improved cost of production and response time. The possibility of utilising wireless power and data transmission techniques for CMOS also allows for the monolithic integration of the communication, power and sensing onto a single chip, which greatly simplifies the post-processing and improves the efficiency of data collection. The ability to concurrently utilise potentiometry and amperometry as an electrochemical technique is explored in this thesis. Potentiometry and amperometry are two of the most common transduction mechanisms for electrochemistry, with their own advantages and disadvantages. Concurrently applying both techniques will allow for real-time calibration of background pH and for improved accuracy of readings. To date, developing circuits for concurrently sensing potentiometry and amperometry has not been explored in the literature. This thesis investigates the possibility of utilising CMOS sensors for wireless potentiometric and amperometric electrochemical sensing. To start with, a review of potentiometry and amperometry is evaluated to understand the key factors behind their operation. A new configuration is proposed whereby the reference electrode for both electrochemistry techniques are shared. This configuration is then compared to both the original configurations to determine any differences in the sensing accuracy through a novel experiment that utilises hydrogen peroxide as a measurement analyte. The feasibility of the configuration with the shared reference electrode is proven and utilised as the basis of the electrochemical configuration for the front end circuits. A unique front-end circuit named DAPPER is developed for the shared reference electrode topology. A review of existing architectures for potentiometry and amperometry is evaluated, with a specific focus on low power consumption for wireless applications. In addition, both the electrochemical sensing outputs are mixed into a single output data channel for use with a near-field communication (NFC). This mixing technique is also further analysed in this thesis to understand the errors arising due to various factors. The system is fabricated on TSMC 180nm technology and consumes 28µW. It measures a linear input current range from 250pA - 0.1µW, and an input voltage range of 0.4V - 1V. This circuit is tested and verified for both electrical and electrochemical tests to showcase its feasibility for concurrent measurements. This thesis then provides the integration of wireless blocks into the system for wireless powering and data transmission. This is done through the design of a circuit named SPACEMAN that consists of the concurrent sensing front-end, wireless power blocks, data transmission, as well as a state machine that allows for the circuit to switch between modes: potentiometry only, amperometry only, concurrent sensing and none. The states are switched through re-booting the circuit. The core size of the electronics is 0.41mm² without the coil. The circuit’s wireless powering and data transmission is tested and verified through the use of an external transmitter and a connected printed circuit board (PCB) coil. Finally, the future direction for ongoing work to proceed towards a fully monolithic electrochemical technique is discussed through the next development of a fully integrated coil-on-CMOS system, on-chip electrodes with the electroplating and microfludics, the development of an external transmitter for powering the device and a test platform. The contributions of this thesis aim to formulate a use for wireless electrochemical sensors capable of concurrent measurements for use in wearable devices.Open Acces

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces