首页|Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

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Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the inci-dent signal and enhance communication performance.However,as the RIS size increases,the phase variations of the received signal across the whole array are nonnegligible in the near-field region,and the channel model mismatch,which will decrease the estimation accuracy,must be considered.In this paper,the lower bound(LB)of the estimated parameter is studied and the impacts of the distance and signal-to-noise ratio(SNR)on LB are then evaluated.Moreover,the impacts of the array scale on LB and spectral efficiency(SE)are also studied.Simu-lation results verify that even in extremely large-scale array systems with infinite SNR,channel model mismatch can still limit estimation ac-curacy.However,this impact decreases with increasing distance.

XL-HRISnear-fieldLBmodel mismatchparameter estimation

LU Zhizheng、HAN Yu、JIN Shi

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National Mobile Communications Research Laboratory,Southeast Uni-versity,Nanjing 210096,China

国家自然科学基金国家自然科学基金国家自然科学基金江苏省自然科学基金Key Technologies Research and Development Program of Jiangsu(Prospective and Key Technologies for Industry)Key Technologies Research and Development Program of Jiangsu(Prospective and Key Technologies for Industry)

623011486234110762261160576BK20230824BE2023022BE2023022-1

2024

10.12142/ZTECOM.202401004

中兴通讯技术(英文版)
中兴通讯股份有限公司,安徽省科技情报研究所

中兴通讯技术(英文版)

影响因子：0.036
ISSN：1673-5188
年,卷(期)：2024.22(1)
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