Learning to Reflect and to Beamform for Intelligent Reflecting Surface With Implicit Channel Estimation

Jiang, Tao; Cheng, Hei Victor; Yu, Wei

doi:10.1109/jsac.2021.3078502

Cited by 178 publications

(100 citation statements)

References 31 publications

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“…Deep Learning(GNN): Adopt a GNN architecture proposed in [24] to capture the interactions among all users and the LEO satellite. The user locations and received pilots Entropy 2022, 24, 326 10 of 14 are directly concatenated as the input feature, and then train the model offline in an unsupervised manner.…”

Section: Benchmark Schemes For Comparisonmentioning

confidence: 99%

“…Fortunately, artificial intelligence (AI) technology provides simple approaches to address such complex problems [21][22][23][24]. Yang et al [22] investigated secure physical communication based on IRS under the condition of time-varying channel coefficients and proposed a deep reinforcement learning approach to jointly optimize both BS and IRS beamforming.…”

Section: Introductionmentioning

confidence: 99%

“…Ge et al [23] established a deep transfer learning framework to solve the beamforming optimization problem for the IRS-assisted MISO system. Jiang et al [24] trained a graph neural network (GNN) architecture to directly map the received pilots to the IRS's phase shifts and BS beamforming matrix. However, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…However, Ref. [24] merges user location and pilots directly, resulting in the location features being easily ignored. In addition, due to the long distance between the satellite and users, the pilots received by the satellite is insensitive to user position information.…”

Section: Introductionmentioning

confidence: 99%

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Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications

Cao

Zhu

Feng

et al. 2022

Entropy

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Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity.

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Section: Benchmark Schemes For Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications

Cao

Zhu

Feng

et al. 2022

Entropy

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“…Several works related to the IRS phase-shift matrix design assume imperfect CSI. In [72,73] the authors propose a ML approach to optimize both the beamformers at the BS and the IRS phase-shift matrix in a multiuser MISO system. Such an approach employs a deep NN to parametrize the mapping from the received pilots to an optimized system configuration, then a graph neural network (GNN) architecture is considered to capture the interactions among the different users in the cellular network.…”

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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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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Interference Nulling Using Reconfigurable Intelligent Surface

Jiang,

Sohrabi,

2023

Intelligent Surfaces Empowered 6G Wireless Network

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Learning to Reflect and to Beamform for Intelligent Reflecting Surface With Implicit Channel Estimation

Cited by 178 publications

References 31 publications

Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications

Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications

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

Interference Nulling Using Reconfigurable Intelligent Surface

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