Unsupervised Learning-Based Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surfaces Aided Wireless Networks

Song, Haobo; Zhang, Mianyi; Gao, Jiabao; Zhong, Caijun

doi:10.1109/lcomm.2020.3041510

Cited by 79 publications

(61 citation statements)

References 18 publications

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“…Specifically, a two-stage NN is implemented and trained offline in an unsupervised manner, and it is then deployed online for real-time predictions. Simulation results exhibit substantial reductions in the computational complexity with satisfactory performance compared to conventional iterative optimization algorithms as in [74] (see [79], Table 1).…”

Section: Single-user Siso Scenariomentioning

confidence: 91%

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Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

et al. 2021

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An intelligent reflective surface (IRS) is a novel and revolutionizing communication technology destined to enable the control of the radio environment. An IRS is a real-time controllable reflectarray with a massive number of low-cost passive elements which introduce a phase shift to the incoming signals from the sources before the propagation towards the destination. This technology introduces the notion of a smart propagation environment with the aim of improving the system performance. In this paper, we provide a comprehensive literature overview on IRS technology, including its basic concepts and reconfiguration, as well as its design aspects and applications for wireless communication systems. We also study the performance metrics and the setups considered in recent publications related to IRS and provide suggestions of future research lines based on still unexplored use cases in the state-of-the-art.

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Section: Single-user Siso Scenariomentioning

confidence: 91%

“…The authors in [79] consider an IRS-aided multiuser MISO downlink system, where the transmit beamforming and the IRS phase-shift matrix are jointly designed to maximize the system sum rate. The solution is a DL-based approach to perform the joint design.…”

Section: Single-user Siso Scenariomentioning

confidence: 99%

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

et al. 2021

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“…2) Deep-learning Based Reflection Design: Deep learning techniques have been recently leveraged to design the IRS passive beamforming without explicit CSI, by exploiting its advantages in learning the non-linear mapping from training data. In particular, the conventional approach is by treating the IRS channel estimation and passive beamforming design as two sequential and separate phases, and applying deep learning techniques for one or both phases (see, e.g., [48], [173], [174]). However, this two-phase design method may not be efficient for the IRS-aided wireless system due to the following reasons.…”

Section: ) Beam Training and Channel Trackingmentioning

confidence: 99%

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications

Zheng¹,

You²,

Mei³

et al. 2021

Preprint

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Intelligent reflecting surface (IRS) has emerged as a key enabling technology to realize smart and reconfigurable radio environment for wireless communications, by digitally controlling the signal reflection via a large number of passive reflecting elements in real time. Different from conventional wireless communication techniques that only adapt to but have no or limited control over dynamic wireless channels, IRS provides a new and cost-effective means to combat the wireless channel impairments in a proactive manner. However, despite its great potential, IRS faces new and unique challenges in its efficient integration into wireless communication systems, especially its channel estimation and passive beamforming design under various practical hardware constraints. In this paper, we provide a comprehensive survey on the up-to-date research in IRS-aided wireless communications, with an emphasis on the promising solutions to tackle practical design issues. Furthermore, we discuss new and emerging IRS architectures and applications as well as their practical design problems to motivate future research.

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“…Another approach that takes the time axis into account is the initialized Gaussian mixture model-the GMM-which is a K Gaussian mixture distribution, with each distribution accounting for different weights as time varies. The first application of this method to text mining is a hybrid classification distribution named Probabilistic Latent Semantic Analysis (PLSA) [17]. The technical term is called GPLSA (combined Gaussian distribution) and is commonly used in recommender systems.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

Sun

2021

Journal of Sensors

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The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.

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Unsupervised Learning-Based Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surfaces Aided Wireless Networks

Cited by 79 publications

References 18 publications

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

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