Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels

Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters

Reconfigurable Receiver Front-Ends for Advanced Telecommunication Technologies

The exponential growth of converging technologies, including augmented reality, autonomous vehicles, machine-to-machine and machine-to-human interactions, biomedical and environmental sensory systems, and artificial intelligence, is driving the need for robust infrastructural systems capable of handling vast data volumes between end users and service providers. This demand has prompted a significant evolution in wireless communication, with 5G and subsequent generations requiring exponentially improved spectral and energy efficiency compared to their predecessors. Achieving this entails intricate strategies such as advanced digital modulations, broader channel bandwidths, complex spectrum sharing, and carrier aggregation scenarios. A particularly challenging aspect arises in the form of non-contiguous aggregation of up to six carrier components across the frequency range 1 (FR1). This necessitates receiver front-ends to effectively reject out-of-band (OOB) interferences while maintaining high-performance in-band (IB) operation. Reconfigurability becomes pivotal in such dynamic environments, where frequency resource allocation, signal strength, and interference levels continuously change. Software-defined radios (SDRs) and cognitive radios (CRs) emerge as solutions, with direct RF-sampling receivers offering a suitable architecture in which the frequency translation is entirely performed in digital domain to avoid analog mixing issues. Moreover, direct RF- sampling receivers facilitate spectrum observation, which is crucial to identify free zones, and detect interferences. Acoustic and distributed filters offer impressive dynamic range and sharp roll off characteristics, but their bulkiness and lack of electronic adjustment capabilities limit their practicality. Active filters, on the other hand, present opportunities for integration in advanced CMOS technology, addressing size constraints and providing versatile programmability. However, concerns about power consumption, noise generation, and linearity in active filters require careful consideration.This thesis primarily focuses on the design and implementation of a low-voltage, low-power RFFE tailored for direct sampling receivers in 5G FR1 applications. The RFFE consists of a balun low-noise amplifier (LNA), a Q-enhanced filter, and a programmable gain amplifier (PGA). The balun-LNA employs noise cancellation, current reuse, and gm boosting for wideband gain and input impedance matching. Leveraging FD-SOI technology allows for programmable gain and linearity via body biasing. The LNA's operational state ranges between high-performance and high-tolerance modes, which are apt for sensitivityand blocking tests, respectively. The Q-enhanced filter adopts noise-cancelling, current-reuse, and programmable Gm-cells to realize a fourth-order response using two resonators. The fourth-order filter response is achieved by subtracting the individual response of these resonators. Compared to cascaded and magnetically coupled fourth-order filters, this technique maintains the large dynamic range of second-order resonators. Fabricated in 22-nm FD-SOI technology, the RFFE achieves 1%-40% fractional bandwidth (FBW) adjustability from 1.7 GHz to 6.4 GHz, 4.6 dB noise figure (NF) and an OOB third-order intermodulation intercept point (IIP3) of 22 dBm. Furthermore, concerning the implementation uncertainties and potential variations of temperature and supply voltage, design margins have been considered and a hybrid calibration scheme is introduced. A combination of on-chip and off-chip calibration based on noise response is employed to effectively adjust the quality factors, Gm-cells, and resonance frequencies, ensuring desired bandpass response. To optimize and accelerate the calibration process, a reinforcement learning (RL) agent is used.Anticipating future trends, the concept of the Q-enhanced filter extends to a multiple-mode filter for 6G upper mid-band applications. Covering the frequency range from 8 to 20 GHz, this RFFE can be configured as a fourth-order dual-band filter, two bandpass filters (BPFs) with an OOB notch, or a BPF with an IB notch. In cognitive radios, the filter’s transmission zeros can be positioned with respect to the carrier frequencies of interfering signals to yield over 50 dB blocker rejection

Next generation analog-to-digital conversion using time-based encoding and digital synthesis techniques

The internet-of-things is a growing market segment which is based on an arrayof portable communication devices with high power efficiency. Advanced semiconductortechnology can easily improve their digital performance, but the samecannot be said for the analog blocks which are vital to their operation. Highperformance analog circuits continue to use conventional design techniques andarchitectures at the expense of power efficiency. Deeply scaled CMOS exaggeratesthis trade-off, opening the door for novel system techniques that take advantage ofthe digital nature of sub-micron transistors. This research focuses on two highlydigital ADCs which can mitigate the short channel effects of limited output swingand low intrinsic gain while also benefiting from process scaling.First, a multi-domain ADC is used to perform quantization on both voltageand time domain signals, relaxing the power-performance trade-off. This hybridapproach can lead to a high resolution, high efficiency data converter in scaledprocess. A prototype ADC was fabricated in 180nm CMOS, showing an SNDRof 73 dB, operating at 20 MHz sampling frequency, with a power consumption of1.28 mW.Next, an automated synthesis process is used to automatically generate a highspeed VCO-based quantizer from verilog code. Stochastic spatial averaging iscombined with a high speed open-loop noise-shaping quantizer to provide enhancedresolution in the presence of device mismatch. Simulation results of a prototypeADC in 180nm CMOS shows an SNDR of 49 dB, operating at 800 MHz samplingfrequency and 50 MHz signal bandwidth.Keywords: data converter, synthesis, verilog, ADC, SAR, TD

Concepts for smart AD and DA converters

This thesis studies the `smart' concept for application to analog-to-digital and digital-to-analog converters. The smart concept aims at improving performance - in a wide sense - of AD/DA converters by adding on-chip intelligence to extract imperfections and to correct for them. As the smart concept can correct for certain imperfections, it can also enable the use of more efficient architectures, thus yielding an additional performance boost. Chapter 2 studies trends and expectations in converter design with respect to applications, circuit design and technology evolution. Problems and opportunities are identfied, and an overview of performance criteria is given. Chapter 3 introduces the smart concept that takes advantage of the expected opportunities (described in chapter 2) in order to solve the anticipated problems. Chapter 4 applies the smart concept to digital-to-analog converters. In the discussed example, the concept is applied to reduce the area of the analog core of a current-steering DAC. It is shown that a sub-binary variable-radix approach reduces the area of the current-source elements substantially (10x compared to state-of-the-art), while maintaining accuracy by a self-measurement and digital pre-correction scheme. Chapter 5 describes the chip implementation of the sub-binary variable-radix DAC and discusses the experimental results. The results confirm that the sub-binary variable-radix design can achieve the smallest published current-source-array area for the given accuracy (12bit). Chapter 6 applies the smart concept to analog-to-digital converters, with as main goal the improvement of the overall performance in terms of a widely used figure-of-merit. Open-loop circuitry and time interleaving are shown to be key to achieve high-speed low-power solutions. It is suggested to apply a smart approach to reduce the effect of the imperfections, unintentionally caused by these key factors. On high-level, a global picture of the smart solution is proposed that can solve the problems while still maintaining power-efficiency. Chapter 7 deals with the design of a 500MSps open-loop track-and-hold circuit. This circuit is used as a test case to demonstrate the proposed smart approaches. Experimental results are presented and compared against prior art. Though there are several limitations in the design and the measurement setup, the measured performance is comparable to existing state-of-the-art. Chapter 8 introduces the first calibration method that counteracts the accuracy issues of the open-loop track-and-hold. A description of the method is given, and the implementation of the detection algorithm and correction circuitry is discussed. The chapter concludes with experimental measurement results. Chapter 9 introduces the second calibration method that targets the accuracy issues of time-interleaved circuits, in this case a 2-channel version of the implemented track-and-hold. The detection method, processing algorithm and correction circuitry are analyzed and their implementation is explained. Experimental results verify the usefulness of the method

Feasibility of a 16bit, 3MSPS multibit per stage pipeline ADC using digital calibration

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Includes bibliographical references (p. 115-116).by Matthew Louis Courcy.B.S.M.Eng

A 12-bit SAR ADC for a flexible tactile sensor

Successive Approximation Register (SAR) Analog-to-Digital Converters (ADC) are some of the most efficient ADC topologies available, allowing excellent performance values at low power consumption across a wide range of sampling frequencies. The proposed ADC is aimed at a tactile sensor application, requiring a low-noise and lowpower solution. In addition, it should have high SNDR to detect even the weakest signals with precision. This thesis presents a 12-bit 400 kS/s SAR ADC implemented in a 180 nm CMOS technology for such a task. The designed SAR ADC uses a hybrid R-C DAC topology consisting of a chargescaling MSB DAC and a voltage-scaling LSB DAC, allowing a good trade-off between power consumption, layout area and performance while keeping the total DAC capacitance under reasonable values. Bootstrapped switches have been implemented to preserve high-linearity during the sampling period. A double-tail dynamic comparator has been designed to obtain a low-noise measurement while ensuring suitable delay values. Finally, regarding the logic, an asynchronous implementation and the conventional switching algorithm provide a simple but effective solution to supply the digital signals of the design. Pre-layout noise simulations with input frequencies around 200 kHz show SNDR values of 72.07 dB, corresponding to an ENOB of 11.67 bits. The total power consumption is 365 ?W while the Walden and Schreier figure-of-merit (FoM) correspond to values of 275 fJ/conversion and 160 dB, respectively