FPGA Implementation of Matrix Inversion Using QRD-RLS Algorithm

Karkooti, Marjan; Cavallaro, Joseph R.; Dick, Chris

doi:10.1109/acssc.2005.1600043

Cited by 115 publications

(80 citation statements)

References 11 publications

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“…Hence, most of the existing work has been focused on the inversion of variable-sized complex-numbered matrices. Matrix inversion based on QR Decomposition Recursive Least Square (QRD-RLS) algorithm has been proposed (Karkooti et al, 2005). In (Myllyla et al, 2005), authors have proposed a Coordinate Rotation Digital Computer (CORDIC) and Squared Givens Rotation (SGR) based Linear MMSE detector while in (Edman & Öwall, 2005) a linear array architecture for SGR implementation has been introduced.…”

Section: Fig 1 System Diagram Of a Modern Radio Platformmentioning

confidence: 99%

ASIP Design and Prototyping for Wireless Communication Applications

Jafri¹,

Baghdadi²,

Jézéquel³

2011

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

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Section: Fig 1 System Diagram Of a Modern Radio Platformmentioning

confidence: 99%

ASIP Design and Prototyping for Wireless Communication Applications

Jafri¹,

Baghdadi²,

Jézéquel³

2011

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

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“…The literature describes a large number of exact methods to compute A −1 ; see the references [27][28][29] for an overview. One of the most efficient methods (in terms of arithmetic operations) that can be implemented in VLSI at low complexity relies on the Cholesky decomposition [6,[30][31][32].…”

Section: Exact Inversion Via the Cholesky Decompositionmentioning

confidence: 99%

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Yin

et al. 2017

J Sign Process Syst

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Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While datadetection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequencyadaptive equalizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.

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“…Every cell performs the multiply-andadd operation. If MMSE is chosen, the input vector should be changed to an 2m × 1 vector y according to the extended model (5). Let y = y…”

Section: A Linear Detection In Systolic Arraymentioning

confidence: 99%

“…In fact, optimal maximum-likelihood (ML) detection in large MIMO systems may not be feasible in real-time applications as its complexity increases exponentially with the number of antennas. Lowcomplexity receivers, employing linear detection or successive spatial-interference cancellation (SIC), are computationally less heavy, and amenable to simple hardware implementation [3]- [5]. However, diversity and error-rate performance of these low-complexity detectors are not comparable to those achieved with ML.…”

Section: Introductionmentioning

confidence: 99%

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Systolic arrays for lattice-reduction-aided mimo detection

2011

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Abstract-Multiple-input, multiple-output (MIMO) technology provides high data rate and enhanced QoS for wireless communications. Since the benefits from MIMO result in a heavy computational load in detectors, the design of low-complexity sub-optimum receivers is currently an active area of research. Lattice-reduction-aided detection (LRAD) has been shown to be an effective low-complexity method with near-ML performance. In this paper we advocate the use of systolic array architectures for MIMO receivers, and in particular we exhibit one of them based on LRAD. The "LLL lattice reduction algorithm" and the ensuing linear detections or successive spatial-interference cancellations can be located in the same array, which is considerably hardware-efficient. Since the conventional form of the LLL algorithm is not immediately suitable for parallel processing, two modified LLL algorithms are considered here for the systolic array. LLL algorithm with full-size reduction (FSR-LLL) is one of the versions more suitable for parallel processing. Another variant is the all-swap lattice-reduction (ASLR) algorithm for complex-valued lattices, which processes all lattice basis vectors simultaneously within one iteration. Our novel systolic array can operate both algorithms with different external logic controls. In order to simplify the systolic array design, we replace the Lovász condition in the definition of LLL-reduced lattice with the looser Siegel condition. Simulation results show that for LRaided linear detections, the bit-error-rate performance is still maintained with this relaxation. Comparisons between the two algorithms in terms of bit-error-rate performance, and average FPGA processing time in the systolic array are made, which shows that ASLR is a better choice for a systolic architecture, especially for systems with a large number of antennas.

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FPGA Implementation of Matrix Inversion Using QRD-RLS Algorithm

Abstract: Abstract-

Cited by 115 publications

References 11 publications

ASIP Design and Prototyping for Wireless Communication Applications

ASIP Design and Prototyping for Wireless Communication Applications

Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection

Systolic arrays for lattice-reduction-aided mimo detection

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