On Hyperparameter Determination for GPR-Based Channel Prediction in IRS-Assisted Wireless Communication Systems

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Norisato Suga;Kazuto Yano;Yafei Hou;Toshikazu Sakano
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引用次数: 0

Abstract

Intelligent reflecting surface (IRS), which can reflect radio waves controlling the phase of incident radio waves, is being investigated for wireless communication in high-frequency bands. To control the reflection characteristic, it is necessary to separately estimate a large number of channel coefficients between transmitting and receiving antennas through each IRS element. This causes significant overhead for the channel estimation. We have proposed a channel prediction method to reduce the overhead using Gaussian process regression with spectral mixture kernel. In Gaussian process regression, the determination of the hyperparameters used to calculate the kernel matrix has a significant impact on prediction accuracy. In this study, we propose validation-based hyperparameter determination for GPR-based channel prediction and evaluate the performance difference between the gradient method and validation.
论 IRS 辅助无线通信系统中基于 GPR 的信道预测的超参数确定
智能反射面(IRS)可通过控制入射无线电波的相位来反射无线电波,目前正被研究用于高频段无线通信。为了控制反射特性,有必要通过每个 IRS 元件分别估算发射和接收天线之间的大量信道系数。这给信道估计带来了巨大的开销。我们提出了一种使用光谱混合核的高斯过程回归的信道预测方法,以减少开销。在高斯过程回归中,用于计算核矩阵的超参数的确定对预测精度有很大影响。在本研究中,我们为基于 GPR 的信道预测提出了基于验证的超参数确定方法,并评估了梯度法与验证法之间的性能差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEICE Communications Express
IEICE Communications Express ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
33.30%
发文量
114
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