129 research outputs found
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
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
Efficient Fast-Convolution-Based Waveform Processing for 5G Physical Layer
This paper investigates the application of fast-convolution (FC) filtering
schemes for flexible and effective waveform generation and processing in the
fifth generation (5G) systems. FC-based filtering is presented as a generic
multimode waveform processing engine while, following the progress of 5G new
radio standardization in the Third-Generation Partnership Project, the main
focus is on efficient generation and processing of subband-filtered cyclic
prefix orthogonal frequency-division multiplexing (CP-OFDM) signals. First, a
matrix model for analyzing FC filter processing responses is presented and used
for designing optimized multiplexing of filtered groups of CP-OFDM physical
resource blocks (PRBs) in a spectrally well-localized manner, i.e., with narrow
guardbands. Subband filtering is able to suppress interference leakage between
adjacent subbands, thus supporting independent waveform parametrization and
different numerologies for different groups of PRBs, as well as asynchronous
multiuser operation in uplink. These are central ingredients in the 5G waveform
developments, particularly at sub-6-GHz bands. The FC filter optimization
criterion is passband error vector magnitude minimization subject to a given
subband band-limitation constraint. Optimized designs with different guardband
widths, PRB group sizes, and essential design parameters are compared in terms
of interference levels and implementation complexity. Finally, extensive coded
5G radio link simulation results are presented to compare the proposed approach
with other subband-filtered CP-OFDM schemes and time-domain windowing methods,
considering cases with different numerologies or asynchronous transmissions in
adjacent subbands. Also the feasibility of using independent transmitter and
receiver processing for CP-OFDM spectrum control is demonstrated
Channel estimation techniques for filter bank multicarrier based transceivers for next generation of wireless networks
A dissertation submitted to Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering (Electrical and Information Engineering), August 2017The fourth generation (4G) of wireless communication system is designed based on the principles of cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) where the cyclic prefix (CP) is used to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) in order to achieve higher data rates in comparison to the previous generations of wireless networks. Various filter bank multicarrier systems have been considered as potential waveforms for the fast emerging next generation (xG) of wireless networks (especially the fifth generation (5G) networks). Some examples of the considered waveforms are orthogonal frequency division multiplexing with offset quadrature amplitude modulation based filter bank, universal filtered multicarrier (UFMC), bi-orthogonal frequency division multiplexing (BFDM) and generalized frequency division multiplexing (GFDM). In perfect reconstruction (PR) or near perfect reconstruction (NPR) filter bank designs, these aforementioned FBMC waveforms adopt the use of well-designed prototype filters (which are used for designing the synthesis and analysis filter banks) so as to either replace or minimize the CP usage of the 4G networks in order to provide higher spectral efficiencies for the overall increment in data rates. The accurate designing of the FIR low-pass prototype filter in NPR filter banks results in minimal signal distortions thus, making the analysis filter bank a time-reversed version of the corresponding synthesis filter bank. However, in non-perfect reconstruction (Non-PR) the analysis filter bank is not directly a time-reversed version of the corresponding synthesis filter bank as the prototype filter impulse response for this system is formulated (in this dissertation) by the introduction of randomly generated errors. Hence, aliasing and amplitude distortions are more prominent for Non-PR.
Channel estimation (CE) is used to predict the behaviour of the frequency selective channel and is usually adopted to ensure excellent reconstruction of the transmitted symbols. These techniques can be broadly classified as pilot based, semi-blind and blind channel estimation schemes. In this dissertation, two linear pilot based CE techniques namely the least square (LS) and linear minimum mean square error (LMMSE), and three adaptive channel estimation schemes namely least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) are presented, analyzed and documented. These are implemented while exploiting the near orthogonality properties of offset quadrature amplitude modulation (OQAM) to mitigate the effects of interference for two filter bank waveforms (i.e. OFDM/OQAM and GFDM/OQAM) for the next generation of wireless networks assuming conditions of both NPR and Non-PR in slow and fast frequency selective Rayleigh fading channel. Results obtained from the computer simulations carried out showed that the channel estimation schemes performed better in an NPR filter bank system as compared with Non-PR filter banks. The low performance of Non-PR system is due to the amplitude distortion and aliasing introduced from the random errors generated in the system that is used to design its prototype filters. It can be concluded that RLS, NLMS, LMS, LMMSE and LS channel estimation schemes offered the best normalized mean square error (NMSE) and bit error rate (BER) performances (in decreasing order) for both waveforms assuming both NPR and Non-PR filter banks.
Keywords: Channel estimation, Filter bank, OFDM/OQAM, GFDM/OQAM, NPR, Non-PR, 5G, Frequency selective channel.CK201
Channel Equalization in Fast-Convolution Filter Bank based Receivers for Professional Mobile Radio
Abstract-Fast convolution processing has recently been proposed as an efficient approach for implementing filter bank multicarrier systems with good spectral containment and high flexibility in adjusting the subchannel bandwidths and center frequencies. These features make fast convolution filter banks (FC-FBs) a particularly interesting choice for multicarrier transmission in challenging radio scenarios like dynamic spectrum access, cognitive radio, and fragmented spectrum use. In this contribution, the target is to compare the performance of the time-domain equalizer with the frequency-domain equalizer implemented through subcarrier processing in LTE-like multicarrier systems. It is shown that integrating the equalization functions with the FC-FB processing leads to an efficient overall implementation in terms of performance and computational complexity
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