Complex-valued QR decomposition implementation for MIMO receivers

Salmela, Perttu; Burian, A.; Sorokin, Harri; Takala, Jarmo

doi:10.1109/icassp.2008.4517889

Cited by 41 publications

(23 citation statements)

References 9 publications

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“…The detection is performed over the free ICI data symbol thanks to the use of a QR (orthogonal-triangle) decomposition [16] of the channel matrix, which is better that the SIS equalizer. The QR equalizer was previously used in the MIMO Receivers [27]. The present proposed algorithm has a good performance for very high Doppler spread (f d T > 10%).…”

mentioning

confidence: 99%

Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains

Hijazi

Ros

2010

IEEE Trans. Commun.

104

118

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Abstract-This paper deals with the case of a high speed mobile receiver operating in an orthogonal-frequency-divisionmultiplexing (OFDM) communication system. Assuming the knowledge of delay-related information, we propose an iterative algorithm for joint multi-path Rayleigh channel complex gains and data recovery in fast fading environments. Each complex gain time-variation, within one OFDM symbol, is approximated by a polynomial representation. Based on the Jakes process, an autoregressive (AR) model of the polynomial coefficients dynamics is built, making it possible to employ the Kalman filter estimator for the polynomial coefficients. Hence, the channel matrix is easily computed, and the data symbol is estimated with free inter-sub-carrier-interference (ICI) thanks to the use of a QRdecomposition of the channel matrix. Our claims are supported by theoretical analysis and simulation results, which are obtained considering Jakes' channels with high Doppler spreads.

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mentioning

confidence: 99%

Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains

Hijazi

Ros

2010

IEEE Trans. Commun.

104

118

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“…In [13], Salmela et al proposed to replace the division in line 9 by a multiplication with the inversed square root of the squared vector norm. The value for r i,i in line 8 is then obtained by multiplying the inversed square root with the squared vector norm.…”

Section: B Developed Hardware Architecturementioning

confidence: 99%

ASIC implementation of a modified QR decomposition for tree search based MIMO detection

Gimmler-Dumont

Schläfer

Wehn

2013

2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS)

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Multiple-antenna systems offer very attractive gains in data rates and transmission reliability. Therefore, they are employed in many modern communication standards. However, the detection and separation of these multiple data streams can be very complex. For tree search based detection methods, a channel preprocessing is mandatory which consists mainly of a QR matrix decomposition.We propose a modification of the standard QR matrix decomposition which simplifies the tree search while not increasing the complexity of the QR decomposition. We present hardware architectures of the original and the modified QR decomposition. The resulting ASIC implementation in 65nm technology runs at a maximum clock frequency of 370 MHz and consumes an area of 0.14mm 2 . The power consumption at the specified clock frequency of 300 MHz is only 6.8mW .

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“…QR decomposition today for instance is applied in multiple-input/ multiple-output (MIMO) channel detection [5][6][7]. Suitable CORDIC implementations were investigated and optimized in a 40 nm CMOS technology [4].…”

Section: Qrd Decomposition Acceleratorsmentioning

confidence: 99%

Highly-Flexible and Optimized, Area: and Energy-Efficient Matrix Decomposition Accelerators based on Givens Algorithms and CORDIC Rotations

Vishnoi¹,

Noll²

2017

J Electr Electron Syst

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IntroductionMatrix-decomposition and -factorization techniques are powerful tools of linear algebra and have applications in topics that span sciences. Specifically, QR, Eigen Value (EVD) and Singular Value (SVD) decomposition are applied in sophisticated digital signal processing for image compression, reconstruction and restoration as well as in decoding, space and noise filtering, parameter estimation and source separation in communications, to name just a few. Interesting and very successful research on systolic architectures for real-time implementations of matrix-decomposition algorithms has been conducted in the 80s and 90s of the last century [1,2]. It was found that in comparison to e.g., Householder-based approaches, Givens-and Jacobi-type algorithms feature a high degree of inherent parallelism and can be implemented by mapping the required rotations onto hardware-efficient Coordinate Rotate Digital Computer (CORDIC) operations [3]. However, during those times VLSI-CMOS technology allowed only for implementation of a single CORDIC processor per chip. Today's very-deep submicron CMOS technologies allow for the realization of high-throughput, low-energy CORDIC macros on a fraction of a square millimeter of silicon area and microwatts of power dissipation [4]. By this, the implementation of high-performance matrix-decomposition modules to be used as number crunching SoC processor sub-macros has become feasible.In this work, new architectures and a new methodology for the quantitative optimization of matrix-decomposition-processor SoCsub-macro implementations for the use in challenging application domains featuring a wide variety of requirements and specifications are elaborated. An attractive target architecture consists of a flexible software-controlled Application Specific Instruction Processor (ASIP) executing the matrix-decomposition algorithms and being accelerated by a large farm of dedicated CORDIC blocks. The architecture template should allow for the decomposition of real-as well as complex valued matrices. Floating-point capability can be achieved by operating the individual CORDIC blocks in block exponent arithmetic. In order to achieve maximum area and especially energy efficiency, the optimization has to be performed concurrently on all levels of CMOS design, from the algorithmic level down to the physical implementation level. Only by that, the interactions between decisions on the different design levels being imposed by the features of today's very-deep submicron CMOS technologies can be properly considered. A key element of this approach is the elaboration of quite accurate, parameterized algebraic cost models for area, throughput, latency and energy of CORDIC macros. QRD Decomposition AcceleratorsQR decomposition today for instance is applied in multiple-input/ multiple-output (MIMO) channel detection [5][6][7]. Suitable CORDIC implementations were investigated and optimized in a 40 nm CMOS technology [4]. Based on the costs of the elementary arithmetic CORDIC sub-functions, a MATLAB-ba...

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Complex-valued QR decomposition implementation for MIMO receivers

Cited by 41 publications

References 9 publications

Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains

Joint data QR-detection and Kalman estimation for OFDM time-varying Rayleigh channel complex gains

ASIC implementation of a modified QR decomposition for tree search based MIMO detection

Highly-Flexible and Optimized, Area: and Energy-Efficient Matrix Decomposition Accelerators based on Givens Algorithms and CORDIC Rotations

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