Low-complexity signal detection and precoding algorithms for multiuser massive MIMO systems

Boukharouba, Abdelhak; Dehemchi, Marwa; Bouhafer, Asma

doi:10.1007/s42452-020-04085-z

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…The performance of the proposed system is compared with existing DNN‐SDR, 26 QR‐ZF, 28 and QAM‐2D‐DSP 29 methods.…”

Section: Resultsmentioning

confidence: 99%

“…Boukharouba et al 28 have presented massive MIMO. It provided efficient detection and pre‐coding algorithms for multiple output wireless systems.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance of the proposed system is compared with existing DNN-SDR, 26 QR-ZF, 28 and QAM-2D-DSP 29 methods. The running time required for the detector to complete the detection of K users' transmitted symbols for single channel recognition with the help of DGNNO-FFA approach.…”

Section: Comparative Analysis Of the Performance Metricsmentioning

confidence: 99%

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Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Eswaramoorthi¹,

Moses²,

Hameed

et al. 2022

Trans Emerging Tel Tech

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The core requirements are generated for sixth generation (6G) wireless communication with low‐latency and ultra‐high speeds to increase the count of ultra scale intelligent factors, like smart cars, mobile root users. The advancement of 6G communication can lead the interference exploitation. To manage the exploitation of uplink multiuser massive (UMM), the multiple‐input and multiple‐output (MIMO) is very difficult to detect the mechanisms, particularly, quadrature amplitude modulation (QAM) signals. To overcome these issues, a novel deep graph neural network optimized with fertile field algorithm based detection model (DGNNO‐FFA) is proposed in this article for uplink multiuser massive MIMO System. The proposed DGNNO‐FFA approach minimizes the channel estimation errors under low signal to noise ratio (SNR) with better bit error rate (BER). Finally, the proposed DGNNO‐FFA approach attains 11.02%, 12.22%, and 25.27% lower BER value, 14.55%, 18.66%, and 29.49% higher energy efficiency, 15.59%, 19.06%, and 29.59% lower NMSE, and 15.59%, 19.06%, and 29.59% lower computational complexity compared with other existing approaches, like deep neural network based semi definite relaxation (DNN‐SDR), QR based zero forcing algorithms (QR‐ZF), and QAM based 2‐dimensional double successive projection model (QAM‐2D‐DSP).

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“…The performance of the proposed system is compared with existing DNN‐SDR, 26 QR‐ZF, 28 and QAM‐2D‐DSP 29 methods.…”

Section: Resultsmentioning

confidence: 99%

“…Boukharouba et al 28 have presented massive MIMO. It provided efficient detection and pre‐coding algorithms for multiple output wireless systems.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Eswaramoorthi¹,

Moses²,

Hameed

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Still, the complexity is more, and the complexity can be reduced further. Sofia Ghacham et al [17] proposed a low-complexity method called Neumann series approximation, the same as [1], but it is done for Zero forcing decoding. The complexity is reduced by truncating the series, but the BER performance needs to be improved.…”

Section: Related Workmentioning

confidence: 99%

“…The complexity is reduced by truncating the series, but the BER performance needs to be improved. Abdelhak Boukharouba et al [17] projected a technique for detection and precoding in uplink transmission with low complexity. It provides a solution for complex matrix inversion by QR decomposition.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity Signal Detection for Massive MIMO in B5G Uplink System

et al. 2023

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Massive Multiple Input Multiple Output (M-MIMO) is realized as a mandatory technique for Beyond Fifth Generation (B5G) wireless networks. In next-generation node B (gNB) uplink transmission, M-MIMO requires a low-complexity signal detection scheme with an increased number of antennas to attain high channel capacity and reliability. To attain close-optimal performance in these B5G systems, the Minimum Mean Square Error (MMSE) detection scheme is preferred at the gNB but it demands a complex matrix inversion concerning the number of users. Hence, this article proposes a Modified Weighted Two Stage (MWTS) iterative algorithm with an appropriate initial solution to realize MMSE detection at reduced complexity. MWTS detection algorithm is formulated by integrating the first half iteration phase of weighted two stage with the previous phase and ignoring the second half iteration. Further to improve the performance of the B5G system, a low-complexity soft decision Viterbi decoder is introduced at gNB. With K users, the proposed modifications display a reduction in computational complexity of 4K 2 +16K as compared to the weighted two stage algorithm of 7K 2 +8K. Simulation results confirm that the proposed MWTS algorithm yields lower complexity and near-optimal performance close to MMSE detection.

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Analysis of a Low Cost and Low Complexity Adaptive Transmission Scheme for Massive MIMO Systems with Interference

Radbord

Abdel-Hafez²,

Amini³

2022

Wireless Pers Commun

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Low-complexity signal detection and precoding algorithms for multiuser massive MIMO systems

Cited by 11 publications

References 21 publications

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Low Complexity Signal Detection for Massive MIMO in B5G Uplink System

Analysis of a Low Cost and Low Complexity Adaptive Transmission Scheme for Massive MIMO Systems with Interference

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