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arXiv 2301.03371eess.SPcs.AIcs.ITcs.LGcs.NImath.IT
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全息超颖面收发器最佳相移的学习

Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers

IITB-Monash Research Academy(IITB-莫纳什研究学院);IIT Bombay(印度理工学院孟买分校);Innopolis University(Innopolis 大学)

Debamita Ghosh, Manjesh K. Hanawal, Nikola Zlatanov

摘要:

全息超颖表面收发器(HMT)是用于增强无线通信系统的覆盖和速率的新兴技术。然而,在HMT辅助的无线通信系统中获取准确的信道状态信息对于实现这些目标至关重要。在本文中,我们提出了一种算法,学习的最佳相移在HMT的远场信道模型。我们所提出的算法利用了远场区域的信道增益结构,并在接收信号中存在噪声的情况下学习最佳相移。我们证明了我们提出的算法估计的最佳相移偏离真实值的概率指数衰减的导频信号的数量。大量的数值模拟验证了理论的保证,也表现出显着的收益相比,国家的最先进的政策。

英文摘要:

Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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