摘要
支持低功耗广覆盖的广域网新兴技术的窄带物联网(NB-IoT)是物联网市场中增长最快的领域之一,其中的关键技术信道估计是准确恢复发送信号的重要步骤。传统的常数插值、线性插值和DFT等插值算法存在着估计精度和算法复杂度之间的问题。针对NB-IoT系统低功耗的要求,提出一种改进的反距离权重(IDW)插值算法。该算法引入距离权重,将周围已知点与待估点之间的距离进行加权平均,即在时域方向上利用周围导频点对非导频点的影响进行插值。仿真结果显示,该算法的精度优于常数插值和线性插值,略差于DFT插值。与传统信道估计算法相比,该算法提高了估计精度,降低了算法复杂度,均方误差(MSE)也显示有良好的性能。同时在不同信道中有近似的性能,有较好的鲁棒性,可以实现应用。
Narrowband Internet of Things(NB-IoT),which supports the emerging technology of wide-area network with low power consumption and wide coverage,is one of the fastest growing areas in the Internet of Things market,where channel estimation,a key technology,is an important step in accurately restoring transmitted signal.Traditional interpolation algorithms,such as constant interpolation,linear interpolation and DFT,have problems between estimation accuracy and algorithm complexity.An improved inverse distance weight(IDW)interpolation algorithm is proposed to meet the requirements of low power consumption in NB IOT system.The distance weight is introduced into the algorithm,and the distance between the known points and the estimated points is weighted and averaged.In other words,the influence of non pilot points is interpolated by surrounding pilot points in time domain.Simulation shows that the accuracy of the proposed algorithm is better than constant interpolation and linear interpolation,and slightly worse than DFT interpolation.Compared with the traditional channel estimation algorithm,the proposed algorithm improves the estimation accuracy,reduces the algorithm complexity,and shows great performance in the mean square error(MSE).At the same time,it has similar performance in different channels and better robustness,which can be applied.
作者
方承志
李晨曦
张子渊
FANG Cheng-zhi;LI Chen-xi;ZHANG Zi-yuan(School of Electronic and Optical Engineering,School of Microelectronics,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《计算机技术与发展》
2021年第9期99-103,共5页
Computer Technology and Development
基金
国家自然科学基金面上资助项目(61271334,61073115)。
关键词
窄带物联网
信道估计
插值
反距离权重
加权幂指数
narrowband Internet of Things
channel estimation
interpolation
inverse distance weight
weighted power exponent
