Low-complexity soft-output detection for massive MIMO using SCBiCG and Lanczos methods

Xiao, Chiyang; Su, Xin; Zeng, Jie; Rong, Liping; Xu, Xiaodong; Wang, Jing

doi:10.1109/cc.2015.7386166

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The diagonal components of the Gram matrix are assumed to be a constant and this approximation is precise if N r ≥ 100. This work presented closed-form BEP for M -QAM modulations using 2-nd order NSE in (5) and extending the BPSK work in [18] to general M -QAM formats for N r > 10N t . To overcome the latter limitation, future research work will consider a higher order NSE, the presence of diagonal components in the Gram matrix as well as other types of inverse matrix operations.…”

Section: Discussionmentioning

confidence: 99%

Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

Chumchewkul

Tsimenidis

2022

IEEE Access

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This paper derives the closed-form bit error probability (BEP) of massive multiple-input, multiple-output (M-MIMO) systems using orthogonal frequency-division multiplexing (OFDM) and zeroforcing (ZF) detection. We improve the BEP accuracy by increasing the Neumann series expansion (NSE) to second order for the system that was previously analyzed in [1] employing a derived probability distribution function (PDF) of the effective noise. The proposed PDF is then utilized to evaluate the BEP, the PDF of output signal-to-noise ratio (SNR), and the outage probability as a function of the output SNR of the system. Furthermore, a simplified closed-form expression for the effective noise PDF, in terms of the Gaussian distribution, and the noise variance are firstly derived in this paper for simplifying the performance analysis. Monte-Carlo simulation results confirm that the outcome from the derived equation and the approximation closely matched those obtained by simulation. In addition, we employ the proposed noise variance to estimate the log-likelihood ratio (LLR) instead of the approximate noise variance for the low-complexity soft-output ZF detection. The computational complexity of the proposed detection is thus significantly reduced, whereas its bit error rate (BER) is lower than that of the classical detection. Focusing on a 10 × 200 Coded-OFDM-M-MIMO system, 97.81% of multiplications, required for producing the LLR from the estimated symbol, were minimized by utilizing the proposed detection. Therefore, the derived equations can be efficiently used for analyzing the performance of OFDM-M-MIMO systems, and reducing the computational complexity of the soft-output ZF detection.

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Section: Discussionmentioning

confidence: 99%

Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

Chumchewkul

Tsimenidis

2022

IEEE Access

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“…Then, MPDQD broke down the transmitted signal's structure sparsely and preceding probability. In 2015, Xiao et al (2015) used SCBiCG together with the Lanczos method to detect the MMSE for UL massive MIMO systems. Hence, there was a reduction in large-scale matrix inversion, which was compulsory for the MMSE to reduce the complexity of computation in order of magnitude that was related to the number of the user equipment.…”

Section: Methodsmentioning

confidence: 99%

Review on various detectors in massive MIMO technology: a performance analysis

Manju

Ganesh

2021

IJPCC

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Purpose Multiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO. Design/methodology/approach This paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method. Findings This paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectors Originality/value This paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

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“…However, the steps of Lanczos method can be divided into initialization and iterations. The iterative process can be concluded in a relationship between the estimated signal and received signal [89] as given bŷ…”

Section: A Linear Detectors Based On the Approximate Matrix Inversionmentioning

confidence: 99%

“…where D denotes the diagonal entries of the Gram matrix G andŷ = H −1 y. Low complexity soft-output detection using the Lanczos method is proposed in [89]. It is capable of outperforming the existing NS approximation based detectors where the MMSE performance has been achieved within few iterations.…”

Section: A Linear Detectors Based On the Approximate Matrix Inversionmentioning

confidence: 99%

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

349

229

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Low-complexity soft-output detection for massive MIMO using SCBiCG and Lanczos methods

Cited by 7 publications

References 10 publications

Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

Closed-Form Bit Error Probability of ZF Detection for OFDM-M-MIMO Systems Using Effective Noise PDF

Review on various detectors in massive MIMO technology: a performance analysis

Massive MIMO Detection Techniques: A Survey

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