Algorithms for Interpolation-Based QR Decomposition in MIMO-OFDM Systems

Cescato, Davide; Bölcskei, Helmut

doi:10.1109/tsp.2010.2104149

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…Recently, the correlations in wireless channel have been utilized to reduce the computational complexity. For example, in [15], [20], an interpolation of QR in frequency domain is implemented, which can be merged with our proposed QRU scheme to utilize both the time and frequency correlations simultaneously. The reconfigurable processor has the capability to support future algorithm-level modifications.…”

Section: Results Discussionmentioning

confidence: 99%

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Energy Efficient Group-Sort QRD Processor With On-Line Update for MIMO Channel Pre-Processing

Zhang

Prabhu

Liu

et al. 2015

IEEE Trans. Circuits Syst. I

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This paper presents a Sorted QR-Decomposition (SQRD) processor for 3GPP LTE-A system. It achieves energy-efficiency by co-optimizing techniques, such as heterogeneous processing, reconfigurable architecture, and dual-supply voltage operation. At algorithm level, a low-complexity hybrid decomposition scheme is adopted, which switches, depending on the energy distribution of spatial channels, between the traditional brute-force SQRD and a proposed group-sort QR-update strategy. A reconfigurable vector processor is accordingly developed to support the adaptive processing with high hardware efficiency. Furthermore, on-chip power management technique is also integrated to obtain real-time power-saving by adapting the voltage supply based on the instantaneous workload. As a proof-of-concept, we implemented the processor using a 65 nm CMOS technology and conducted post-layout simulation. The proposed SQRD processor occupies 0.71 core area and has a throughput of up to 69 MQRD/s. Compared to the brute-force approach, an energy reduction of 10 61.8% is achieved.

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Section: Results Discussionmentioning

confidence: 99%

“…However, the computation involved is expensive and has to be performed for each sub-carrier. To tackle this, various approximative approaches are proposed to reduce the complexity at the price of some performance degradation, such as hold- [13], [14], interpolating- [15].…”

Section: B Channel Pre-processing-qrdmentioning

confidence: 99%

Energy Efficient Group-Sort QRD Processor With On-Line Update for MIMO Channel Pre-Processing

Zhang

Prabhu

Liu

et al. 2015

IEEE Trans. Circuits Syst. I

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“…Finite Impulse Response functions (=Laurent polynomials) are of numerous applications in communications control and signal processing see e.g. [12], [13], [14], [15], [27], [28], [39], [42]. 1.2.…”

Section: Introductionmentioning

confidence: 99%

Characterizations of families of rectangular, finite impulse response, para-unitary systems

Alpay

Jørgensen

Lewkowicz

2016

J. Appl. Math. Comput.

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We here study Finite Impulse Response (FIR) rectangular, not necessarily causal, systems which are (para)-unitary on the unit circle (=the class U ). First, we offer three characterizations of these systems. Then, introduce a description of all FIRs in U , as copies of a real polytope, parametrized by the dimensions and the McMillan degree of the FIRs.Finally, we present six simple ways (along with their combinations) to construct, from any FIR, a large family of FIRs, of various dimensions and McMillan degrees, so that whenever the original system is in U , so is the whole family.A key role is played by Hankel matrices.1 β=0,Hence, in Engineering terminology Φ(t) (and often F (z)) is referred to as Finite Impulse Response (i.e. the support of Φ(t) is finite).Moreover F (z) will be called causal whenever 1 ≥ q (i.e. Φ(t) ≡ 0 for all 0 > t) and strictly causal if 0 ≥ q. Similarly, (strictly) anti-causal when (q ≥ n + 1) q ≥ n.

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“…Such characteristics have previously been exploited to develop algorithms for a range of applications such as adaptive receivers, adaptive equalizers and adaptive beamforming, among others [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering

Medina¹,

Sampaio‐Neto²

2013

Signal Processing

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a b s t r a c tIn this paper an inverse QR decomposition based recursive least-squares algorithm for linearly constrained minimum variance filtering is proposed. The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures. Its performance is illustrated by simulations of a blind receiver for a multicarrier CDMA communication system and compared with previously proposed inverse QR decomposition recursive least-squares algorithms.

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Algorithms for Interpolation-Based QR Decomposition in MIMO-OFDM Systems

Cited by 23 publications

References 21 publications

Energy Efficient Group-Sort QRD Processor With On-Line Update for MIMO Channel Pre-Processing

Energy Efficient Group-Sort QRD Processor With On-Line Update for MIMO Channel Pre-Processing

Characterizations of families of rectangular, finite impulse response, para-unitary systems

An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering

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