Deep Neural Network Based Detection Algorithm for High-Order Modulation in Uplink Massive MIMO

Hou, Huijun; Li, Lin; Meng, Weixiao

doi:10.1109/iwcmc51323.2021.9498882

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Hou et al 26 have presented deep learning for massive MIMO uplink detectors. The main intention was to offer insights on deep learning base detectors to a generalist of wireless communications.…”

Section: Literature Surveymentioning

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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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%

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

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Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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Deep Neural Network Based Detection Algorithm for High-Order Modulation in Uplink Massive MIMO

Cited by 2 publications

References 15 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

Deep Learning for Massive MIMO Uplink Detectors

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