Ordered MMSE–SIC via sorted QR decomposition in ill conditioned large-scale MIMO channels

Kobayashi, Ricardo Tadashi; Abrão, Taufik

doi:10.1007/s11235-015-0123-5

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…However, by saving these flops, the norm may not be calculated correctly in line 10, as it is an approximation valid when

{bold-italicq}_{i}

and

{bold-italicq}_{j}

are orthogonal, which may not be guaranteed depending on the input matrix. This effect has been reported in [16], where the use of Algorithm 1 makes the MMSE combined with sorted‐interference‐cancellation ineffective in high correlated antennas scenarios, where the Bit Error Rate performance versus SNR curves have its slope inverted under high SNR regime.…”

Section: Modified Gs‐qrdmentioning

confidence: 66%

“…Hence, the motivation in this paper is to understand the wireless channel correlation effects and negative impact on the QR‐based MIMO detectors. As an example of the negative impact of the GS‐QRD method applied to MIMO detection can be found in [16] and references inside, where it is demonstrated that the MMSE detection combined with the ordered‐interference cancellation becomes ineffective under high correlated antennas scenarios. In this case, it was observed the inversion of the BER × SNR curve for high signal‐to‐noise ratio (SNR) region and high or very‐high antenna correlation.…”

Section: Introductionmentioning

confidence: 99%

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Stability analysis in Gram‐Schmidt QR decomposition

Kobayashi

Abrão

2016

IET signal process.

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“…However, by saving these flops, the norm may not be calculated correctly in line 10, as it is an approximation valid when

{bold-italicq}_{i}

and

{bold-italicq}_{j}

Section: Modified Gs‐qrdmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Stability analysis in Gram‐Schmidt QR decomposition

Kobayashi

Abrão

2016

IET signal process.

Self Cite

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“…In accordance with the received signal in Eq. ( 1) and the correlated channel model in [19,20], and [21], the estimated uplink channel vector can be written as:…”

Section: Channel Estimationmentioning

confidence: 99%

“…The least square channel estimation method was used; however, this method is not suitable in the case of pilot contamination reduction as it encounters a drought of orthogonality between the required and interfering pilots. In contrast, the linear minimum mean square error (MMSE) beamforming detector with low complexity has received significant attention from researchers seeking to upgrade conventional wireless communication systems to 5G [20,21]. The main objective of a linear MMSE beamforming receiver is reducing the mean-square error of the estimated signal versus the transmitted signal.…”

Section: Introductionmentioning

confidence: 99%

Beamforming with 2D-AOA estimation for pilot contamination reduction in massive MIMO

Ali

Ismail²,

Nordin³

et al. 2018

Telecommun Syst

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The upcoming fifth-generation wireless mobile systems are projected to be commercialized in the year 2020 and are set to play the main role in massive multiple input-multiple output (mMIMO) integrated with the technologies of beamforming antenna arrays. However, the performance of mMIMO is constrained by inter-cell interference from adjacent cells triggered by the reused pilot, an issue termed pilot contamination. To address this issue, this paper focuses on mitigating pilot contamination by implementing a suboptimal spatial source detection method based on the beamforming approach using a two-dimensionunitary estimation of the signal parameters through rotational invariance techniques algorithm. We jointly used 2D-AOA information (azimuth and elevation angles) and statistical channel estimations at the BSs to identify the channels correlation condition and evaluate the sum rate performance of users based on AOA. The detected signals from the uniform rectangular array are used to segregate the required signal from the interfering signal without any change to the pilot construction of the training signals. The performance of the minimum mean squared error beamforming (MMSE-beamforming) technique in the multi-cell mMIMO system of the aforementioned method is numerically evaluated and then compared with conventional methods that depend only on pilot identity information. The simulation reveals that the achievable sum rate gains of 2D-AOA and the pilot identity information techniques (best case) with respect to deterministic MMSE are 96.3% and 85.4%, respectively, thus showing the prospect of eliminating the majority of pilot contamination using the 2D-AOA estimation. Keywords 2-D angle of arrival • 2D-UESPRIT • Massive MIMO • MMSE-beamforming • Pilot contamination • Uniform rectangular array B Ehab Ali

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“…The complexity of LLL algorithm depends on the matrix size and also the antenna/channel correlation index ρ. In [79] the complexity of this algorithm was evaluated by numerical experiment, considering δ = 3 4 and equal number of Tx and Rx antennas. The function that describe the complexity in numbers of flops has been obtained by fitting, and given by:…”

Section: Complexity Analysismentioning

confidence: 99%

Performance, efficiency and complexity in multiple access large-scale MIMO Systems.

Mussi¹

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First, I would like to thank God for giving me health and opportunity to develop this work. I express my gratitude to my advisor, Prof. Taufik Abrão, for his continuous willingness to meet the demands of this work, sharing his knowledge and experience. His guidance in this process has given me professional and personal growth. I would like to thank my family for the love and encouragement always dedicated to me: my parents Jorge and Nair, my girlfriend Marcela and my sister Tatiana. Last but not least, I thank my friends and colleagues at the State University of Londrina for their support throughout this work: Ricardo, Edno, Álvaro, José Carlos and Aislan; besides Jair and Luiz by the partnership.

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Ordered MMSE–SIC via sorted QR decomposition in ill conditioned large-scale MIMO channels

Cited by 11 publications

References 26 publications

Stability analysis in Gram‐Schmidt QR decomposition

Stability analysis in Gram‐Schmidt QR decomposition

Beamforming with 2D-AOA estimation for pilot contamination reduction in massive MIMO

Performance, efficiency and complexity in multiple access large-scale MIMO Systems.

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