On hyperparameter determination for GPR-based channel prediction in IRS-assisted wireless communication systems

被引:0
|
作者
Suga, Norisato [1 ,2 ]
Yano, Kazuto [1 ]
Hou, Yafei [1 ,3 ]
Sakano, Toshikazu [1 ]
机构
[1] ATR Wave Engn Labs, 2-2 Hikaridai, Kyoto 6190288, Japan
[2] Shibaura Inst Technol, Fac Engn, 7-5 Toyosu, Koto, Tokyo 1358548, Japan
[3] Okayama Univ, Fac Environm Life Nat Sci & Technol, 3-1-1 Tsushima Naka,Kita Ku, Okayama, Okayama 7008530, Japan
来源
IEICE COMMUNICATIONS EXPRESS | 2024年 / 13卷 / 08期
关键词
IRS; RIS; channel prediction; Gaussian process regression;
D O I
10.23919/comex.2024XBL0058
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 hyper parameters used to calculate the kernel matrix has a significant impact on prediction accuracy. In this study, we propose validation-based hyper parameter determination for GPR-based channel prediction and evaluate the performance difference between the gradient method and validation.
引用
收藏
页码:315 / 318
页数:4
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