一种多用户上行放大转发中继系统中快速收敛的信道估计方法  

A fast algorithm with convergence for channel estimation in multi-user uplink amplify-and-forward relay system

作　　者：林和昀[1] 袁超伟[1] 杜建和[2] 

机构地区：[1]北京邮电大学信息与通信工程学院,北京100876 [2]中国传媒大学信息工程学院,北京100024

出　　处：《物理学报》2016年第21期35-43,共9页Acta Physica Sinica

基　　金：国家高技术研究发展计划(批准号:2015AA01A705;2014AA01A701);中国传媒大学理工科规划项目(批准号:3132016XNG1618)资助的课题~~

摘　　要：针对传统交替最小二乘算法存在的收敛缓慢问题,本文在多用户上行放大转发中继系统中基于Levenberg Marquardt(LM)算法,提出了一种能够快速收敛的信道估计方法,实现了用户-中继信道和中继-基站信道的独立估计.在基站,通过对中继多次放大转发的信号进行建模,构造出具有平行因子结构的三维信号张量模型,并采用LM算法对该模型进行拟合,从而得到系统中两跳链路的信道状态信息.理论分析与仿真结果表明,与已有二线性交替最小二乘方法相比,所提方法具有近乎相同的估计精度;当中继放大因子矩阵为随机矩阵或者包含近似共线性相关列时,所提方法具有更快的收敛速度.Recently, tensor models（or multi-way arrays） play a vital role in many applications, such as wireless communication systems, blind source separation, machine learning, signal（audio, image, speech） processing, chemometrics, data mining,arithmetic complexity, environmental sciences, etc. Parallel factor（PARAFAC） analysis, also known as canonical polyadic decomposition, is a common name for low rank decomposition of tensors. A traditional way to fit the PARAFAC model is the alternating least squares（ALS） algorithm, which can transform a nonlinear optimization problem into some independent linear least squares problems. However, the ALS scheme for computing the decomposition of the tensor is known to converge slowly if one or some modes include nearly collinear columns. Particularly, if the collinearity is presented in all modes, the ALS will end in a ＂convergence bottleneck＂.Hence, it is necessary to develop a robust and fast algorithm to compute the decomposition of the tensor. In this paper, a novel channel estimation algorithm using the Levenberg Marquardt（LM） method based on a third-order tensor model is presented in a multi-user uplink amplify-and-forward（AF） relay system. As the relay nodes all operate with half-duplex mode to aid the transmission, the overall transmission period is partitioned into two transmission subprocesses. In the first transmission sub-process, the users transmit channel training sequence to the relay nodes. This stage requires time block once. During the second transmission sub-process, a set of diagonal amplifying factor matrices are utilized by the relay nodes to amplify the received data. Then, the relay nodes transmit each of the amplified data to the base station. This stage requires time blocks K times. With the help of the channel training sequence and the relay amplifying factor matrices, the received data at the base station can be stacked up into a third-order PARAFAC model.And then based on this tensor model an LM channel estimation algorithm is p

关 键 词：张量模型 信道估计 Levenberg Marquardt算法 放大转发中继 

分 类 号：TN919.3[电子电信—通信与信息系统]