BASEBAND RADIO MODEM DESIGN USING GRAPHICS PROCESSING UNITS

A modern radio or wireless communications transceiver is programmed via software and firmware to change its functionalities at the baseband. However, the actual implementation of the radio circuits relies on dedicated hardware, and the design and implementation of such devices are time consuming and challenging. Due to the need for real-time operation, dedicated hardware is preferred in order to meet stringent requirements on throughput and latency. With increasing need for higher throughput and shorter latency, while supporting increasing bandwidth across a fragmented spectrum, dedicated subsystems are developed in order to service individual frequency bands and specifications. Such a dedicated-hardware-intensive approach leads to high resource costs, including costs due to multiple instantiations of mixers, filters, and samplers. Such increases in hardware requirements in turn increases device size, power consumption, weight, and financial cost. If it can meet the required real-time constraints, a more flexible and reconfigurable design approach, such as a software-based solution, is often more desirable over a dedicated hardware solution. However, significant challenges must be overcome in order to meet constraints on throughput and latency while servicing different frequency bands and bandwidths. Graphics processing unit (GPU) technology provides a promising class of platforms for addressing these challenges. GPUs, which were originally designed for rendering images and video sequences, have been adapted as general purpose high-throughput computation engines for a wide variety of application areas beyond their original target domains. Linear algebra and signal processing acceleration are examples of such application areas. In this thesis, we apply GPUs as software-based, baseband radios and demonstrate novel, software-based implementations of key subsystems in modern wireless transceivers. In our work, we develop novel implementation techniques that allow communication system designers to use GPUs as accelerators for baseband processing functions, including real-time filtering and signal transformations. More specifically, we apply GPUs to accelerate several computationally-intensive, frontend radio subsystems, including filtering, signal mixing, sample rate conversion, and synchronization. These are critical subsystems that must operate in real-time to reliably receive waveforms. The contributions of this thesis can be broadly organized into 3 major areas: (1) channelization, (2) arbitrary resampling, and (3) synchronization. 1. Channelization: a wideband signal is shared between different users and channels, and a channelizer is used to separate the components of the shared signal in the different channels. A channelizer is often used as a pre-processing step in selecting a specific channel-of-interest. A typical channelization process involves signal conversion, resampling, and filtering to reject adjacent channels. We investigate GPU acceleration for a particularly efficient form of channelizer called a polyphase filterbank channelizer, and demonstrate a real-time implementation of our novel channelizer design. 2. Arbitrary resampling: following a channelization process, a signal is often resampled to at least twice the data rate in order to further condition the signal. Since different communication standards require different resampling ratios, it is desirable for a resampling subsystem to support a variety of different ratios. We investigate optimized, GPU-based methods for resampling using polyphase filter structures that are mapped efficiently into GPU hardware. We investigate these GPU implementation techniques in the context of interpolation (integer-factor increases in sampling rate), decimation (integer-factor decreases in sampling rate), and rational resampling. Finally, we demonstrate an efficient implementation of arbitrary resampling using GPUs. This implementation exploits specialized hardware units within the GPU to enable efficient and accurate resampling processes involving arbitrary changes in sample rate. 3. Synchronization: incoming signals in a wireless communications transceiver must be synchronized in order to recover the transmitted data properly from complex channel effects such as thermal noise, fading, and multipath propagation. We investigate timing recovery in GPUs to accelerate the most computationally intensive part of the synchronization process, and correctly align the incoming data symbols in the receiver. Furthermore, we implement fully-parallel timing error detection to accelerate maximum likelihood estimation

Design and Realization of Fully-digital Microwave and Mm-wave Multi-beam Arrays with FPGA/RF-SOC Signal Processing

There has been a constant increase in data-traffic and device-connections in mobile wireless communications, which led the fifth generation (5G) implementations to exploit mm-wave bands at 24/28 GHz. The next-generation wireless access point (6G and beyond) will need to adopt large-scale transceiver arrays with a combination of multi-input-multi-output (MIMO) theory and fully digital multi-beam beamforming. The resulting high gain array factors will overcome the high path losses at mm-wave bands, and the simultaneous multi-beams will exploit the multi-directional channels due to multi-path effects and improve the signal-to-noise ratio. Such access points will be based on electronic systems which heavily depend on the integration of RF electronics with digital signal processing performed in Field programmable gate arrays (FPGA)/ RF-system-on-chip (SoC). This dissertation is directed towards the investigation and realization of fully-digital phased arrays that can produce wideband simultaneous multi-beams with FPGA or RF-SoC digital back-ends. The first proposed approach is a spatial bandpass (SBP) IIR filter-based beamformer, and is based on the concepts of space-time network resonance. A 2.4 GHz, 16-element array receiver, has been built for real-time experimental verification of this approach. The second and third approaches are respectively based on Discrete Fourier Transform (DFT) theory, and a lens plus focal planar array theory. Lens based approach is essentially an analog model of DFT. These two approaches are verified for a 28 GHz 800 MHz mm-wave implementation with RF-SoC as the digital back-end. It has been shown that for all proposed multibeam beamformer implementations, the measured beams are well aligned with those of the simulated. The proposed approaches differ in terms of their architectures, hardware complexity and costs, which will be discussed as this dissertation opens up. This dissertation also presents an application of multi-beam approaches for RF directional sensing applications to explore white spaces within the spatio-temporal spectral regions. A real-time directional sensing system is proposed to capture the white spaces within the 2.4 GHz Wi-Fi band. Further, this dissertation investigates the effect of electro-magnetic (EM) mutual coupling in antenna arrays on the real-time performance of fully-digital transceivers. Different algorithms are proposed to uncouple the mutual coupling in digital domain. The first one is based on finding the MC transfer function from the measured S-parameters of the antenna array and employing it in a Frost FIR filter in the beamforming backend. The second proposed method uses fast algorithms to realize the inverse of mutual coupling matrix via tridiagonal Toeplitz matrices having sparse factors. A 5.8 GHz 32-element array and 1-7 GHz 7-element tightly coupled dipole array (TCDA) have been employed to demonstrate the proof-of-concept of these algorithms

Superimposed training for single carrier transmission in future mobile communications

The amount of wireless devices and wireless traffic has been increasing exponentially for the last ten years. It is forecasted that the exponential growth will continue without saturation till 2020 and probably further. So far, network vendors and operators have tackled the problem by introducing new evolutions of cellular macro networks, where each evolution has increased the physical layer spectral efficiency. Unfortunately, the spectral efficiency of the physical layer is achieving the Shannon-Hartley limit and does not provide much room for improvement anymore. However, considering the overhead due to synchronization and channel estimation reference symbols in the context of physical layer spectral efficiency, we believe that there is room for improvement. In this thesis, we will study the potentiality of superimposed training methods, especially data-dependent superimposed training, to boost the spectral efficiency of wideband single carrier communications even further. The main idea is that with superimposed training we can transmit more data symbols in the same time duration as compared to traditional time domain multiplexed training. In theory, more data symbols means more data bits which indicates higher throughput for the end user. In practice, nothing is free. With superimposed training we encounter self-interference between the training signal and the data signal. Therefore, we have to look for iterative receiver structures to separate these two or to estimate both, the desired data signal and the interfering component. In this thesis, we initiate the studies to find out if we truly can improve the existing systems by introducing the superimposed training scheme. We show that in certain scenarios we can achieve higher spectral efficiency, which maps directly to higher user throughput, but with the cost of higher signal processing burden in the receiver. In addition, we provide analytical tools for estimating the symbol or bit error ratio in the receiver with a given parametrization. The discussion leads us to the conclusion that there still remains several open topics for further study when looking for new ways of optimizing the overhead of reference symbols in wireless communications. Superimposed training with data-dependent components may prove to provide extra throughput gain. Furthermore, the superimposed component may be used for, e.g., improved synchronization, low bit-rate signaling or continuous tracking of neighbor cells. We believe that the current systems could be improved by using the superimposed training collectively with time domain multiplexed training

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion