空中IRS

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  • 2022-11-28 13:46
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由以上的内容启发,智能反射面(Intelligent Reflecting Surface,IRS)最近被认为是一个具有前景的新范例,用以实现为B5G和6G系统提供智能且可重构的无线信道/无线电传输环境
Aerial-RIS-main.zip
  • Aerial-RIS-main
  • DNN_prediction
  • data_set_small.csv
    753.8KB
  • Aerial_RIS_DNN_OP_prediction.ipynb
    37.2KB
  • ReadMe.md
    1B
  • main_DNN_prediction.py
    5.1KB
  • Outage_Analysis
  • gengausslegquadrule.m
    6.1KB
  • nsumk.m
    1.3KB
  • Phi2.m
    422B
  • ReadMe.md
    1B
  • main_OP_ana_sim.m
    5.7KB
  • Demo_OP.pdf
    43.1KB
  • Aerial_RIS_manuscript.pdf
    552.2KB
  • README.md
    2.1KB
  • Aerial_RIS_DNN_OP_prediction_Colaboratory.pdf
    63.2KB
内容介绍
# Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems **Tri Nhu Do, Georges Kaddoum, Thanh Luan Nguyen, Daniel Benevides da Costa, and Zygmunt J. Haas** _IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021_ ## Abstract In this paper, we propose and investigate an aerial reconfigurable intelligent surface (aerial-RIS)-aided wireless com- munication system. Specifically, considering practical composite fading channels, we characterize the air-to-ground (A2G) links by Namkagami-m small-scale fading and inverse-Gamma large- scale shadowing. To investigate the delay-limited performance of the proposed system, we derive a tight approximate closed- form expression for the end-to-end outage probability (OP). Next, considering a mobile environment, where performance analysis is intractable, we rely on machine learning-based performance prediction to evaluate the performance of the mobile aerial- RIS-aided system. Specifically, taking into account the three- dimensional (3D) spatial movement of the aerial-RIS, we build a deep neural network (DNN) to accurately predict the OP. We show that: (i) fading and shadowing conditions have strong impact on the OP, (ii) as the number of reflecting elements increases, aerial-RIS achieves higher energy efficiency (EE), and (iii) the aerial-RIS-aided system outperforms conventional relaying systems. ## Paper - [PrePrint arXiv](https://arxiv.org/abs/2106.05380) - [IEEExplore](https://ieeexplore.ieee.org/document/9569450) ## Source code ### Outage performance analysis - [Demo by MATLAB](https://github.com/trinhudo/Aerial-RIS/blob/main/Outage_Analysis/Demo_OP.pdf) - [Source code](https://github.com/trinhudo/Aerial-RIS/tree/main/Outage_Analysis) ### Outage probability prediction using deep neural network (DNN) - [Demo by Google Colab](https://github.com/trinhudo/AerialRIS/blob/main/Aerial_RIS_DNN_OP_prediction_Colaboratory.pdf) - [ipynb](https://github.com/trinhudo/AerialRIS/blob/main/DNN_prediction/Aerial_RIS_DNN_OP_prediction.ipynb) - [Source code](https://github.com/trinhudo/Aerial-RIS/tree/main/DNN_prediction)
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