4 research outputs found
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
GPU-based Acceleration of Symbol Timng Recovery
This paper presents a novel implementation of graphics
processing unit (GPU) based symbol timing recovery using
polyphase interpolators to detect symbol timing error. Symbol
timing recovery is a compute intensive procedure that detects
and corrects the timing error in a coherent receiver. We
provide optimal sample-time timing recovery using a maximum
likelihood (ML) estimator to minimize the timing error.
This is an iterative and adaptive system that relies on
feedback, therefore, we present an accelerated implementation
design by using a GPU for timing error detection (TED),
enabling fast error detection by exploiting the 2D filter structure
found in the polyphase interpolator. We present this hybrid/
heterogeneous CPU and GPU architecture by computing
a low complexity and low noise matched filter (MF) while
simultaneously performing TED. We then compare the performance
of the CPU vs. GPU based timing recovery for different
interpolation rates to minimize the error and improve
the detection by up to a factor of 35. We further improve the
process by utilizing GPU optimization and performing block
processing to improve the throughput even more, all while
maintaining the lowest possible sampling rate.Laboratory for Telecommunications SciencesNational Science Foundation (NSF
Applications in Electronics Pervading Industry, Environment and Society
This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs