Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO

Fang, Licai; Xu, Lingyun; Huang, Defeng

doi:10.1109/lwc.2015.2504366

Cited by 64 publications

(51 citation statements)

References 8 publications

(20 reference statements)

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“…Therefore, we use the complexity with matched filter algorithm as the lower bound for comparison, which has the complexity of O(N r N t ) for every subcarrier. Table II is the summary of complexity comparison between the exact MMSE, the matched filter, the algorithms in [14], [15] and the proposed algorithm. In the table, the term N 2 t N r corresponds to the computing of Gram matrix G n and I is the number of terms in the Neumann Series expansion in [14].…”

Section: Computational Complexity Comparisonmentioning

confidence: 99%

“…Table II is the summary of complexity comparison between the exact MMSE, the matched filter, the algorithms in [14], [15] and the proposed algorithm. In the table, the term N 2 t N r corresponds to the computing of Gram matrix G n and I is the number of terms in the Neumann Series expansion in [14]. The proposed algorithm has great computation saving compared to the exact implementation, while it has comparable complexity to the matched filter.…”

Section: Computational Complexity Comparisonmentioning

confidence: 99%

“…The proposed algorithm has great computation saving compared to the exact implementation, while it has comparable complexity to the matched filter. For example, when N t = 16, N r = 128, N = 256 and D = 16, up to 84% and 77% of computation saving can be obtained by the proposed algorithm compared to the exact implementation and the algorithm in [14] …”

Section: Computational Complexity Comparisonmentioning

confidence: 99%

“…entries, the channel vectors become orthogonal to each other with the consequence that H H H converges to a scaled identity matrix, which enables simple detection algorithms such as a matched filter to achieve very good performance. However, for practical medium-size massive MIMO systems, the minimum mean square error (MMSE) with soft-output algorithms [13], [14] are often preferred due to its better bit error rate (BER) performance over matched filter and relatively low complexity [13]. When the orthogonal frequency division multiplexing (OFDM) is employed in such systems, the per-tone based MMSE detection incurs very high complexity.…”

Section: Introductionmentioning

confidence: 99%

“…The overall complexity is O(N N 2 t N r ), where N is the number of subcarriers, and N t and N r are the numbers of the transmit and receive antennas, respectively. Recently, [14] proposes an algorithm which can reduce the total complexity to O(N N t N r ) level, but it is still much higher than the matched filter algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A low cost interpolation based detection algorithm for medium-size massive MIMO-OFDM systems

Fang

Huang

et al. 2017

2017 17th International Symposium on Communications and Information Technologies (ISCIT)

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Abstract-The great potential of exploiting millimeter wave (mmwave) frequency spectrum for emerging fifth-generation (5G) wireless networks has motivated the study of massive multipleinput multiple-output (MIMO) for achieving high data rate. For medium-size massive MIMO with orthogonal frequency division multiplexing (OFDM) uplink systems, the minimum mean square error (MMSE) based soft-output detector is often used due to its better bit error rate (BER) performance compared to the matched filter detector. Although the multipath channel can be converted into a set of parallel flat-fading channels by using OFDM thus reducing the complexity of receiver design, the tone by tone (per subcarrier) detection methods based on the state-of-the-art low complexity MMSE still incur considerably high computational complexity since the number of tones is typically very large. To reduce the complexity, the interpolationbased matrix inversion algorithms for small-size MIMO-OFDM systems have been proposed, which compute the matrix inversion by interpolating separately the adjoint and determinant. In this paper, we find that the (regularized) Gram matrix inversions have strong correlation between different subcarriers. By exploiting this strong correlation, we propose a linear interpolation based MMSE detection algorithm that directly interpolates the inverted MMSE matrices for a small number of subcarriers to obtain matrix inversions for all other subcarriers, thereby significantly reducing the number of matrix inversion required. Extensive simulations show that with small BER performance loss compared to the exact MMSE detector, the proposed algorithm can reduce the complexity to the level of the matched filter algorithm.

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Section: Computational Complexity Comparisonmentioning

confidence: 99%

Section: Computational Complexity Comparisonmentioning

confidence: 99%

Section: Computational Complexity Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A low cost interpolation based detection algorithm for medium-size massive MIMO-OFDM systems

Fang

Huang

et al. 2017

2017 17th International Symposium on Communications and Information Technologies (ISCIT)

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Retracted: Target tracking and risk avoidance system for intelligent driving system based on 5G signal anomaly detection

Lan

2020

Int J Communication

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The above article, published online on 29 June 2020 on Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/dac.4518) has been retracted by agreement between the journal Editor‐in‐Chief, Mohammad Obaidat and John Wiley & Sons Ltd. The retraction has been agreed following an investigation based on allegations raised by a third party. It found that the article contains inconsistencies including irrelevant citations and was not reviewed in line with the journal’s peer review standards. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Therefore, the article’s conclusions must be considered insufficiently supported.

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Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

Khurshid

Imran

Wakeel

2023

Trans Emerging Tel Tech

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SummaryMassive MIMO systems are a key technology for enhancing the capacity and reliability of wireless communication systems. However, to unlock the full potential of massive MIMO systems, accurate channel state information is crucial at the receivers. In practice, channel estimation errors are inevitable, leading to significant performance degradation in traditional massive MIMO equalizers that assume perfect channel state information. In this work, we investigate the performance of approximate massive MIMO detectors, specifically the Gauss‐Seidel and Neumann series, over a time‐invariant channel with imperfect channel state information. We derive the expressions of signal‐to‐interference‐plus‐noise ratio and average bit error rate for approximate massive MIMO detectors in a unicellular environment using M‐ary quadrature amplitude modulation. Additionally, we compare the performance of linear detectors, including zero‐forcing, minimum‐mean‐squared‐error, and maximal‐ratio combining, with that of the approximate detectors. Through extensive Monte Carlo numerical simulations and various system configurations, we demonstrate that the Gauss‐Seidel detector, under imperfect channel state information, achieves comparable bit error rate performance to the zero‐forcing and minimum‐mean‐squared‐error detectors while requiring less computational complexity. Our study suggests that the Gauss‐Seidel detector has the potential to be used in massive MIMO systems in the presence of imperfect channel state information, highlighting its practical relevance in real‐world scenarios.

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Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO

Cited by 64 publications

References 8 publications

A low cost interpolation based detection algorithm for medium-size massive MIMO-OFDM systems

A low cost interpolation based detection algorithm for medium-size massive MIMO-OFDM systems

Retracted: Target tracking and risk avoidance system for intelligent driving system based on 5G signal anomaly detection

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

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