89 research outputs found

    Digital and Mixed Domain Hardware Reduction Algorithms and Implementations for Massive MIMO

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    Emerging 5G and 6G based wireless communications systems largely rely on multiple-input-multiple-output (MIMO) systems to reduce inherently extensive path losses, facilitate high data rates, and high spatial diversity. Massive MIMO systems used in mmWave and sub-THz applications consists of hundreds perhaps thousands of antenna elements at base stations. Digital beamforming techniques provide the highest flexibility and better degrees of freedom for phased antenna arrays as compared to its analog and hybrid alternatives but has the highest hardware complexity. Conventional digital beamformers at the receiver require a dedicated analog to digital converter (ADC) for every antenna element, leading to ADCs for elements. The number of ADCs is the key deterministic factor for the power consumption of an antenna array system. The digital hardware consists of fast Fourier transform (FFT) cores with a multiplier complexity of (N log2N) for an element system to generate multiple beams. It is required to reduce the mixed and digital hardware complexities in MIMO systems to reduce the cost and the power consumption, while maintaining high performance. The well-known concept has been in use for ADCs to achieve reduced complexities. An extension of the architecture to multi-dimensional domain is explored in this dissertation to implement a single port ADC to replace ADCs in an element system, using the correlation of received signals in the spatial domain. This concept has applications in conventional uniform linear arrays (ULAs) as well as in focal plane array (FPA) receivers. Our analysis has shown that sparsity in the spatio-temporal frequency domain can be exploited to reduce the number of ADCs from N to where . By using the limited field of view of practical antennas, multiple sub-arrays are combined without interferences to achieve a factor of K increment in the information carrying capacity of the ADC systems. Applications of this concept include ULAs and rectangular array systems. Experimental verifications were done for a element, 1.8 - 2.1 GHz wideband array system to sample using ADCs. This dissertation proposes that frequency division multiplexing (FDM) receiver outputs at an intermediate frequency (IF) can pack multiple (M) narrowband channels with a guard band to avoid interferences. The combined output is then sampled using a single wideband ADC and baseband channels are retrieved in the digital domain. Measurement results were obtained by employing a element, 28 GHz antenna array system to combine channels together to achieve a 75% reduction of ADC requirement. Implementation of FFT cores in the digital domain is not always exact because of the finite precision. Therefore, this dissertation explores the possibility of approximating the discrete Fourier transform (DFT) matrix to achieve reduced hardware complexities at an allowable cost of accuracy. A point approximate DFT (ADFT) core was implemented on digital hardware using radix-32 to achieve savings in cost, size, weight and power (C-SWaP) and synthesized for ASIC at 45-nm technology

    Spectral Efficiency of One-Bit Sigma-Delta Massive MIMO

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    We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta ( \Sigma \Delta ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the \Sigma \Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the \Sigma \Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the \Sigma \Delta approach for both MRC and zero-forcing receivers

    Millimeter and sub-millimeter wave radiometers for atmospheric remote sensing from CubeSat platforms

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    2018 Fall.Includes bibliographical references.To view the abstract, please see the full text of the document

    DEVELOPMENT OF AN UWB RADAR SYSTEM

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    An ultra-wideband radar system is built at the University of Tennessee with the goal to develop a ground penetrating radar (GPR). The radar is required to transmit and receive a very narrow pulse signal in the time domain. The bistatic radar transmits a pulse through an ultrawide spiral antenna and receives the pulse by a similar antenna. Direct sampling is used to improve the performance of the impulse radar allowing up to 1.5 GHz of bandwidth to be used for signal processing and target detection with high resolution. Using direct sampling offers a less complex system design than traditional lower sample rate, super-heterodyne systems using continuous wave or step frequency methods while offering faster results than conventional equivalent time sampling techniques that require multiple data sets and significant post-processing. These two points are particularly important for a system that may be used in the field in potentially dangerous environments. Direct sampling radar systems, while still frequency limited, are continually improving their upper frequencies boundaries due to more power efficient, higher sampling rate analog to digital converters (ADCs) which relates directly to better subsurface resolution for potential target detection

    Doctor of Philosophy

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    dissertationAdvancements in process technology and circuit techniques have enabled the creation of small chemical microsystems for use in a wide variety of biomedical and sensing applications. For applications requiring a small microsystem, many components can be integrated onto a single chip. This dissertation presents many low-power circuits, digital and analog, integrated onto a single chip called the Utah Microcontroller. To guide the design decisions for each of these components, two specific microsystems have been selected as target applications: a Smart Intravaginal Ring (S-IVR) and an NO releasing catheter. Both of these applications share the challenging requirements of integrating a large variety of low-power mixed-signal circuitry onto a single chip. These applications represent the requirements of a broad variety of small low-power sensing systems. In the course of the development of the Utah Microcontroller, several unique and significant contributions were made. A central component of the Utah Microcontroller is the WIMS Microprocessor, which incorporates a low-power feature called a scratchpad memory. For the first time, an analysis of scaling trends projected that scratchpad memories will continue to save power for the foreseeable future. This conclusion was bolstered by measured data from a fabricated microcontroller. In a 32 nm version of the WIMS Microprocessor, the scratchpad memory is projected to save ~10-30% of memory access energy depending upon the characteristics of the embedded program. Close examination of application requirements informed the design of an analog-to-digital converter, and a unique single-opamp buffered charge scaling DAC was developed to minimize power consumption. The opamp was designed to simultaneously meet the varied demands of many chip components to maximize circuit reuse. Each of these components are functional, have been integrated, fabricated, and tested. This dissertation successfully demonstrates that the needs of emerging small low-power microsystems can be met in advanced process nodes with the incorporation of low-power circuit techniques and design choices driven by application requirements

    Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models

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    This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition. By exploiting the resulting conditional Bussgang decomposition, we derive parameterized linear minimum mean square error (MMSE) estimators for the considered generative latent variable models. In this context, we explore leveraging model-based structural features to reduce memory and complexity overhead associated with the proposed estimators. Furthermore, we devise necessary training adaptations, enabling direct learning of the generative models from quantized pilot observations without requiring ground-truth channel samples during the training phase. Through extensive simulations, we demonstrate the superiority of our introduced estimators over existing state-of-the-art methods for coarsely quantized systems, as evidenced by significant improvements in mean square error (MSE) and achievable rate metrics

    CMOS Data Converters for Closed-Loop mmWave Transmitters

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    With the increased amount of data consumed in mobile communication systems, new solutions for the infrastructure are needed. Massive multiple input multiple output (MIMO) is seen as a key enabler for providing this increased capacity. With the use of a large number of transmitters, the cost of each transmitter must be low. Closed-loop transmitters, featuring high-speed data converters is a promising option for achieving this reduced unit cost.In this thesis, both digital-to-analog (D/A) and analog-to-digital (A/D) converters suitable for wideband operation in millimeter wave (mmWave) massive MIMO transmitters are demonstrated. A 2 76 bit radio frequency digital-to-analog converter (RF-DAC)-based in-phase quadrature (IQ) modulator is demonstrated as a compact building block, that to a large extent realizes the transmit path in a closed-loop mmWave transmitter. The evaluation of an successive-approximation register (SAR) analog-to-digital converter (ADC) is also presented in this thesis. Methods for connecting simulated and measured performance has been studied in order to achieve a better understanding about the alternating comparator topology.These contributions show great potential for enabling closed-loop mmWave transmitters for massive MIMO transmitter realizations
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