Three-Dimensional Geometry-Based Stochastic Channel Modeling for Intelligent Reflecting Surface-Assisted UAV MIMO Communications

Jiang, Hao; He, Ruisi; Ruan, Chengyao; Zhou, Jie; Chang, Daina

doi:10.1109/lwc.2021.3113669

Cited by 34 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, an increasing amount of research attention has been dedicated to propose an accurate RANTN channel model. For instance, the very recent works [163], [164] adopt the elliptic cylinders to model the propagation environment among the HAP/UAV transmitter, TRIS, and the ground mobile receiver, while the work [165] considers a cluster structure to describe the scattering environment between the UAV transmitter and the TRIS, which indicates that the signals from the UAV will first impinge on the cluster before arriving at the TRIS. Moreover, Xiong et al [166] proposed the TRISassisted A2G mmWave channel model by treating the RIS as a virtual cluster and RIS units as virtual scatterers.…”

Section: A Practical Channel Modelingmentioning

confidence: 99%

Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

Qiao

Kammoun

et al. 2022

Proc. IEEE

View full text Add to dashboard Cite

| Nonterrestrial communications have emerged as a key enabler for seamless connectivity in the upcoming generation networks. This kind of network can support high data rate communications among aerial platforms (i.e., unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites) and cellular networks, achieving anywhere and anytime connections. However, there are many practical implementation limitations, especially overload power consumption, high probability of blockage, and dynamic propagation environment. Fortunately, the recent technology reconfigurable intelligent surface (RIS) is expected to be one of the most cost-efficient solutions to address such issues. RIS with low-cost elements can bypass blockages and create multiple line-of-sight (LoS) links and provide controllable communication channels. In this article, we present a comprehensive literature review on the RIS-assisted nonterrestrial networks (RANTNs). First, the framework of the RANTNs is introduced with detailed discussion about distinct properties of RIS in NTNs and the two Manuscript

show abstract

Section: A Practical Channel Modelingmentioning

confidence: 99%

Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

Qiao

Kammoun

et al. 2022

Proc. IEEE

View full text Add to dashboard Cite

show abstract

“…Still, they utilized ideal channel assumptions in their model. In [27], the authors suggested a 3D geometrybased stochastic multi-input multi-output (MIMO) channel model for intelligent reflecting surface (IRS)-aided UAV communications. The IRS is mounted outside the buildings to help the UAV transmitter reflect its signals to the ground level receiver and improve communication through passive beamforming.…”

Section: Non Ai-based Solutionsmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks' high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.INDEX TERMS Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), and reinforcement learning (RL).

show abstract

“…path loss method) needs to carry out large numbers of measurements, which mainly suitable for specific scenarios. Compared with deterministic models, geometry-based stochastic model (GBSM) has been widely used for describing a variety of IRS-assisted communication scenarios due to its high accuracy and generality [19]- [23]. In [19], a twin cluster channel model was developed for massive multiple-input multiple-output (MIMO) communication scenarios.…”

Section: A Related Workmentioning

confidence: 99%

“…Furthermore, based on [21], a high-altitude platform (HAP)-MIMO channel model was proposed in [22]. In [23], a 3D cluster-based model was proposed for UAV-MIMO channels. Remarkably, despite the aforementioned works [13]- [23] investigate the impacts of IRS on the statistical properties of wireless channel, the nature of IRS, i.e., creating a smart radio environment between the transmitter (Tx) and the receiver (Rx), is completely ignored.…”

Section: A Related Workmentioning

confidence: 99%

Modeling and Analysis of MIMO Multipath Channels With Aerial Intelligent Reflecting Surface

et al. 2022

IEEE J. Select. Areas Commun.

Self Cite

View full text Add to dashboard Cite

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) onecylinder model is first developed for AIRS-assisted multipleinput multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades in the magnitude of CIR. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide

show abstract

Three-Dimensional Geometry-Based Stochastic Channel Modeling for Intelligent Reflecting Surface-Assisted UAV MIMO Communications

Cited by 34 publications

References 10 publications

Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

AI-Enabled UAV Communications: Challenges and Future Directions

Modeling and Analysis of MIMO Multipath Channels With Aerial Intelligent Reflecting Surface

Contact Info

Product

Resources

About