TY - JOUR
T1 - Training Design for Channel Estimation in Uplink Cloud Radio Access Networks
AU - Hu, Qiang
AU - Peng, Mugen
AU - Mao, Zhendong
AU - Xie, Xinqian
AU - Poor, H. Vincent
N1 - Funding Information:
The work of X. Xie, M. Peng, and W. Wang was supported in part by the National Natural Science Foundation of China (Grant No. 61361166005), the National Basic Research Program of China (973 Program) (Grant No. 2013CB336600), and the Chinese State Major Science and Technology Special Projects (Grant No. 2016ZX03001020-006). The work of H. V. Poor was supported in part by the U.S. National Science Foundation under Grant ECCS-1343210. (Corresponding author: M. Peng.)
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2016/7/1
Y1 - 2016/7/1
N2 - In this paper, a training design and channel estimation scheme is considered for uplink cloud radio access networks (C-RANs) consisting of multiple user equipments (UEs), remote radio heads (RRHs), and a centralized baseband unit (BBU) pool. Since most signal processing functions in C-RANs are moved from RRHs to the BBU pool, the individual channels over the links between UEs and RRHs and the links between RRHs and the BBU pool cannot be estimated directly. To address this issue, segment training based individual channel estimation for C-RANs is proposed in this paper, in which channel state information acquisition is performed through two consecutive segments. By using the Kalman filter, the sequential minimum mean-square-error (SMMSE) estimator is developed to efficiently estimate the individual channel states through prior knowledge of long-term channel correlation statistics and the latest radio channel state. A training structure design subject to a power constraint is obtained by minimizing the mean-square-error (MSE) of the SMMSE estimator. Since the MSE is insufficient to fully evaluate the overall performance of C-RANs, the uplink ergodic capacity is derived to exploit the impact of channel estimation on the data transmission by taking the estimation errors into consideration, and the tradeoff between the lengths of two segment training sequences is optimized by maximizing the corresponding spectral efficiency. Furthermore, the Cramér-Rao bound is used to evaluate the proposed SMMSE estimator's performance. Simulation results show that the SMMSE estimator and the corresponding training design can effectively decrease MSE and significantly increase the quality and efficiency of data transmission in C-RANs.
AB - In this paper, a training design and channel estimation scheme is considered for uplink cloud radio access networks (C-RANs) consisting of multiple user equipments (UEs), remote radio heads (RRHs), and a centralized baseband unit (BBU) pool. Since most signal processing functions in C-RANs are moved from RRHs to the BBU pool, the individual channels over the links between UEs and RRHs and the links between RRHs and the BBU pool cannot be estimated directly. To address this issue, segment training based individual channel estimation for C-RANs is proposed in this paper, in which channel state information acquisition is performed through two consecutive segments. By using the Kalman filter, the sequential minimum mean-square-error (SMMSE) estimator is developed to efficiently estimate the individual channel states through prior knowledge of long-term channel correlation statistics and the latest radio channel state. A training structure design subject to a power constraint is obtained by minimizing the mean-square-error (MSE) of the SMMSE estimator. Since the MSE is insufficient to fully evaluate the overall performance of C-RANs, the uplink ergodic capacity is derived to exploit the impact of channel estimation on the data transmission by taking the estimation errors into consideration, and the tradeoff between the lengths of two segment training sequences is optimized by maximizing the corresponding spectral efficiency. Furthermore, the Cramér-Rao bound is used to evaluate the proposed SMMSE estimator's performance. Simulation results show that the SMMSE estimator and the corresponding training design can effectively decrease MSE and significantly increase the quality and efficiency of data transmission in C-RANs.
KW - Cloud radio access networks
KW - Cramér-Rao bound
KW - channel estimation
KW - training design
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U2 - 10.1109/TSP.2016.2539126
DO - 10.1109/TSP.2016.2539126
M3 - Article
AN - SCOPUS:84975222178
SN - 1053-587X
VL - 64
SP - 3324
EP - 3337
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 13
M1 - 7426860
ER -