Decentralized Signal Detection via Expectation Propagation Algorithm for Uplink Massive MIMO Systems

Zhang, Zhenyu; Hua, Li; Dong, Yuanyuan; Wang, Xiyuan; Dai, Xiaoming

doi:10.1109/tvt.2020.3008151

Cited by 27 publications

(31 citation statements)

References 15 publications

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“…Here, we develop a customized GAEP detector for performance improvement. Recently, EP algorithm is widely applied to approximate the true posterior with modest complexity for traditional MIMO [46] and single carrier [47] systems. The performance advantage of EP motivates us to develop more efficient receiver algorithms in our model to overcome the co-channel user interference and the self-interference in the delay-Doppler domain.…”

Section: A Centralized Detectormentioning

confidence: 99%

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

Deng

González

et al. 2023

IEEE Trans. Veh. Technol.

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This paper investigates an uplink coordinated multipoint (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by the remote radio head (RRH) in front of them and the RRH behind them simultaneously. We apply orthogonal time frequency space (OTFS) modulation for each user to exploit the degrees of freedom arising from both the delay and Doppler domains. As the signals received by the RRHs in front of and behind the users experience respectively positive and negative Doppler frequency shifts, our proposed OTFS-based SCMA (OBSCMA) with CoMP system can effectively harvest extra Doppler and spatial diversity for better performance. Based on maximum likelihood (ML) detector, we analyze the single-user average bit error rate (ABER) bound as the benchmark of the ABER performance for our proposed OBSCMA with CoMP system. We also develop a customized Gaussian approximation with expectation propagation (GAEP) algorithm for multi-user detection and propose efficient algorithm structures for centralized and decentralized detectors. Our proposed OBSCMA with CoMP system leads to stronger performance than the existing solutions. The proposed centralized and decentralized detectors exhibit effective reception and robustness under channel state information uncertainty.

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Section: A Centralized Detectormentioning

confidence: 99%

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

Deng

González

et al. 2023

IEEE Trans. Veh. Technol.

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“…The results show that the proposed VLSI architecture can achieve a competitive balance between the performance and the computational complexity. In [144], a DBP based on expectation propagation algorithm (EPA) is proposed for signal detection in UL massive MIMO. The BS is partitioned into multiple independent antenna clusters, each associated with analog and digital modulation circuitry, RF chains, and computing hardware where local channel estimation and signal detection are executed.…”

Section: B Detection In Cell-free Massive Mimomentioning

confidence: 99%

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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“…Zhang et al 31 have presented a signal detection based on decentralized baseband processing under the expectation propagation approach framework for uplink massive multiple input multiple output schemes. The presented framework divides base station antennas as several independent antenna clusters, each one connected to analog with digital modulation circuits, independent radio‐frequency chains, and computer hardware.…”

Section: Literature Surveymentioning

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

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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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Decentralized Signal Detection via Expectation Propagation Algorithm for Uplink Massive MIMO Systems

Cited by 27 publications

References 15 publications

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications

Deep Learning for Massive MIMO Uplink Detectors

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

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