149 research outputs found
Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations
Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to
be one of the key technologies in next-generation multi-user cellular systems,
based on the upcoming 3GPP LTE Release 12 standard, for example. In this work,
we propose - to the best of our knowledge - the first VLSI design enabling
high-throughput data detection in single-carrier frequency-division multiple
access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate
matrix inversion algorithm relying on a Neumann series expansion, which
substantially reduces the complexity of linear data detection. We analyze the
associated error, and we compare its performance and complexity to those of an
exact linear detector. We present corresponding VLSI architectures, which
perform exact and approximate soft-output detection for large-scale MIMO
systems with various antenna/user configurations. Reference implementation
results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to
achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale
MIMO system. We finally provide a performance/complexity trade-off comparison
using the presented FPGA designs, which reveals that the detector circuit of
choice is determined by the ratio between BS antennas and users, as well as the
desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin
Improving Fixed-Point Implementation of QR Decomposition by Rounding-to-Nearest
QR decomposition is a key operation in many
current communication systems. This paper shows how to reduce
the area of a fixed-point QR decomposition implementation
based on Givens rotations by using a new number representation
system. This new representation allows performing round-tonearest
at the same cost of truncation. Consequently, the
rounding errors of the results are halved, which allows it to
reduce the word-length by one bit. This reduction positively
impacts on the area, delay and power consumption of the design.Ministry of Education and Science of Spain and Junta of AndalucĂa under contracts TIN2013-42253-P
and TIC-1692, respectively, and Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
High-Throughput FPGA Implementation of QR Decomposition
Munoz, S.D.; Hormigo, J. "High-Throughput FPGA Implementation of QR Decomposition" IEEE Transactions on in Circuits and Systems II: Express Briefs,vol.62, no.9, pp.861-865, Sept. 2015 doi: 10.1109/TCSII.2015.2435753This brief presents a hardware design to achieve
high-throughput QR decomposition, using Givens Rotation Method. It utilizes a new two-dimensional systolic array architecture
with pipelined processing elements, which are based on the COordinate Rotation DIgital Computer (CORDIC) algorithm. CORDIC computes vector rotations through shifts and additions.
This approach allows a continuous computation of QR factorizations with simple hardware. A fixed-point FPGA architecture for 4 x 4 matrices has been optimized by balancing the number of CORDIC iterations with the final error. As a result, compared to other previous proposals for FPGA, our design achieves at least 50% more throughput, and much less resource utilization.Ministry of Education and Science of Spain and Junta of Andalucia under contracts TIN2013-42253-P and
P07-TIC-02630, respectively
Using reconfigurable computing technology to accelerate matrix decomposition and applications
Matrix decomposition plays an increasingly significant role in many scientific and engineering applications. Among numerous techniques, Singular Value Decomposition (SVD) and Eigenvalue Decomposition (EVD) are widely used as factorization tools to perform Principal Component Analysis for dimensionality reduction and pattern recognition in image processing, text mining and wireless communications, while QR Decomposition (QRD) and sparse LU Decomposition (LUD) are employed to solve the dense or sparse linear system of equations in bioinformatics, power system and computer vision. Matrix decompositions are computationally expensive and their sequential implementations often fail to meet the requirements of many time-sensitive applications.
The emergence of reconfigurable computing has provided a flexible and low-cost opportunity to pursue high-performance parallel designs, and the use of FPGAs has shown promise in accelerating this class of computation. In this research, we have proposed and implemented several highly parallel FPGA-based architectures to accelerate matrix decompositions and their applications in data mining and signal processing. Specifically, in this dissertation we describe the following contributions:
⢠We propose an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices.
⢠We implement a floating-point Hestenes-Jacobi architecture for SVD, which is capable of analyzing arbitrary sized matrices.
⢠We introduce a novel deeply pipelined reconfigurable architecture for QRD, which can be dynamically configured to perform either Householder transformation or Givens rotation in a manner that takes advantage of the strengths of each.
⢠We design a configurable architecture for sparse LUD that supports both symmetric and asymmetric sparse matrices with arbitrary sparsity patterns.
⢠By further extending the proposed hardware solution for SVD, we parallelize a popular text mining tool-Latent Semantic Indexing with an FPGA-based architecture.
⢠We present a configurable architecture to accelerate Homotopy l1-minimization, in which the modification of the proposed FPGA architecture for sparse LUD is used at its core to parallelize both Cholesky decomposition and rank-1 update.
Our experimental results using an FPGA-based acceleration system indicate the efficiency of our proposed novel architectures, with application and dimension-dependent speedups over an optimized software implementation that range from 1.5ĂĂ to 43.6ĂĂ in terms of computation time
Hardware implementation of multiple-input multiple-output transceiver for wireless communication
This dissertation proposes an efficient hardware implementation scheme for iterative multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) transceiver. The transmitter incorporates linear precoder designed with instantaneous channel state information (CSI). The receiver implements MMSE-IC (minimum mean square error interference cancelation) detector, channel estimator, low-density parity-check (LDPC) decoder and other supporting modules. The proposed implementation uses QR decomposition (QRD) of complex-valued matrices with four co-ordinate rotation digital computer (CORDIC) cores and back substitution to achieve the best tradeoff between resource and throughput. The MIMO system is used in field test and the results indicate that the instantaneous CSI varies very fast in practices and the performance of linear precoder designed with instantaneous CSI is limited. Instead, statistic CSI had to be used.
This dissertation also proposes a higher-rank principle Kronecker model (PKM). That exploits the statistic CSI to simulate the fading channels. The PKM is constructed by decomposing the channel correlation matrices with the higher-order singular value decomposition (HOSVD) method. The proposed PKM-HOSVD model is validated by extensive field experiments conducted for 4-by-4 MIMO systems in both indoor and outdoor environments. The results confirm that the statistic CSI varies slowly and the PKM-HOSVD will be helpful in the design of linear precoders. --Abstract, page iv
A general framework for efficient FPGA implementation of matrix product
Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance systems are required by the developers for fast processing of computationally intensive applications. Reconfigurable hardware devices in the form of Filed-Programmable Gate Arrays (FPGAs) have been proposed as viable system building blocks in the construction of high performance systems at an economical price. Given the importance and the use of matrix algorithms in scientific computing applications, they seem ideal candidates to harness and exploit the advantages offered by FPGAs. In this paper, a system for matrix algorithm cores generation is described. The system provides a catalog of efficient user-customizable cores, designed for FPGA implementation, ranging in three different matrix algorithm categories: (i) matrix operations, (ii) matrix transforms and (iii) matrix decomposition. The generated core can be either a general purpose or a specific application core. The methodology used in the design and implementation of two specific image processing application cores is presented. The first core is a fully pipelined matrix multiplier for colour space conversion based on distributed arithmetic principles while the second one is a parallel floating-point matrix multiplier designed for 3D affine transformations.Peer reviewe
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