Deep Expectation-Maximization for Joint MIMO Channel Estimation and Signal Detection

Zhang, Yiqing; Sun, Jianyong; Xue, Jiang; Li, Geoffrey Ye; Zhao, Xu

doi:10.1109/tsp.2022.3205478

Cited by 15 publications

(10 citation statements)

References 58 publications

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“…Reference [47] introduces a novel approach to modelling environmental noise using the Student's t ‐distribution rather than a Gaussian distribution, enhancing robustness against outliers. This complex model is made tractable through a generalized EM (GEM) algorithm which jointly and robustly estimates the channel matrix and transmitted signals, showing superior performance compared to conventional independent methods.…”

Section: Deep Learning In Conventional Communicationsmentioning

confidence: 99%

Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Mao,

Mu,

Liang

et al. 2023

IET Communications

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With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.

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Section: Deep Learning In Conventional Communicationsmentioning

confidence: 99%

Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Mao,

Mu,

Liang

et al. 2023

IET Communications

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“…Traditional iterative detection algorithms are designed based on the assumption that the channel model is subject to a specific distribution, thus the performance is unsatisfactory in variable environments. To tackle the issue, DL-based detectors have been proposed [1,[15][16][17][18][19][20]. In refs.…”

Section: Mimo Detectionmentioning

confidence: 99%

“…Several model-driven DL-based methods based on conventional iterative detectors have also been investigated in recent years [17][18][19][20]. The authors of ref.…”

Section: Mimo Detectionmentioning

confidence: 99%

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Deep Learning for MIMO Communications

Cai,

Hu,

Zhang

et al. 2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

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Recently, deep learning (DL) is becoming a key feature of next-generation multiple-input multiple-output (MIMO) transceiver design with learning and inference capabilities embedded in the network, which achieves greatly enhanced system performance. Popular topics include end-to-end (E2E) learning for transceiver design, deep reinforcement learning (DRL) for communications, and model-driven deep unfolding techniques. In particular, E2E learning treats the communication system design as an E2E data reconstruction task that seeks to jointly optimize transceiver components, so that encoding and decoding are fostered by the learned weights of deep neural networks (DNN). E2E learning can be employed to solve various problems in MIMO communications, such as channel state information (CSI) feedback, beamforming, signal detection, and channel estimation. Moreover, DRL has been widely applied to solve high-dimensional non-convex optimization problems in designing the transceiver. However, these DNNs generally suffer from an inability to be interpreted or generalized, and they often lack performance guarantees. To overcome such drawbacks, substantial researches have proposed to unfold the iterations of an iterative optimization algorithm into a layer-wise structure analogous to a DNN. Inspired by the great potential of these DL methods, it is important to investigate AI-empowered transceivers for future MIMO systems.

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“…Here, a downlink CSI is reconstructed using sounding reference signals of uplink medium. In [15]- [18], a wideband channel estimation technique is proposed for spatial multiplexing in a massive MIMO system with low-resolution analog-to-digital converters (ADCs). This technique reduces propagation delay and improves inter-symbol interference.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based channel estimation with application to 5G and beyond networks

Jayashankar,

Shobha

2024

IJRES

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Channel state information (CSI) feedback estimation for a downlink medium in a massive multiple input multiple output (MIMO) system is an essential and critical task to improve channel capacity and performance yield, especially in a frequency division duplex (FDD) multiplexing system. However, spectral efficiency degradation is a massive issue due to high channel feedback overhead. This work proposes a deep learning-based channel estimation (DLCE) model to improve channel reconstruction efficiency and channel overhead reduction accuracy. The proposed deep learning (DL) mechanism consists of encoder and decoder network where encoder network is utilized to compress CSI matrices whereas decoder network is used to decompress obtained CSI matrices. Here, inverse discrete Fourier transform (IDFT) method is utilized to convert CSI matrices of frequency domain into CSI matrices of delay domain. Simulation results are evaluated between uplink and downlink medium in the massive MIMO system considering a co-operation in science and technology (COST) 2,100 model. Here, a significant improvement in correlation and normalized mean square error (NMSE) results is observed. The proposed DLCE model shows superior performance against varied channel estimation techniques in terms of NMSE and correlation efficiency.

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Deep Expectation-Maximization for Joint MIMO Channel Estimation and Signal Detection

Cited by 15 publications

References 58 publications

Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Deep Learning for MIMO Communications

Deep learning-based channel estimation with application to 5G and beyond networks

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