Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

被引：18

作者：

Xie, Xinqian ^{[1
]}

Peng, Mugen ^{[1
]}

Wang, Wenbo ^{[1
]}

Poor, H. Vincent ^{[2
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100088, Peoples R China

[2] Princeton Univ, Sch Engn & Appl Sci, Princeton, NJ 08544 USA

来源：

IEEE SIGNAL PROCESSING LETTERS | 2015年 / 22卷 / 08期

基金：

中国国家自然科学基金; 美国国家科学基金会; 北京市自然科学基金;

关键词：

Channel estimation; cloud radio access networks; 2-WAY RELAY NETWORKS;

D O I：

10.1109/LSP.2014.2380776

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：

To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio head prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed. Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the average effective signal-to-noise ratio (AESNR) in C-RANs.

引用

页码：1060 / 1064

页数：5