@inproceedings{9c420f9a432a4c9baa72ecd0d7030a69,
title = "Cluster formation with data-assisted channel estimation in cloud-radio access networks",
abstract = "A dilemma in cloud radio access networks (C-RANs) is how to keep a balance between the performance and the efficiency of centralized processing. To solve this problem, the joint design of channel estimation and cluster formation are studied in this paper. In particular, a data-assisted channel estimation scheme is used to reduce the redundant cost of training sequences, and C-RAN clusters are formed by the remote radio heads (RRHs) to provide efficient cooperation strategies. To ensure the performance of channel estimation and data transmission, the cluster formation and the channel estimation are optimized jointly. An iterative channel estimation scheme is designed by using convex optimization and the Broyden-Fletcher- Goldfarb-Shanno (BFGS) algorithm jointly. Moreover, a utility function of cluster formation can be established based on the estimates and the mean squared error (MSE) of our proposed channel estimation algorithm, and the cluster formation of RRHs can be formulated as a coalitional formation game. Finally, the simulation results are shown to evaluate the performance of our proposed algorithms.",
keywords = "Channel estimation, Cloud-radio access networks, Cluster formation, Game theory, Optimization",
author = "Yourong Ban and Mingfeng Xu and Zhongyuan Zhao and Yong Li and Zhiguo Ding",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 ; Conference date: 19-03-2017 Through 22-03-2017",
year = "2017",
month = may,
day = "10",
doi = "10.1109/WCNC.2017.7925674",
language = "British English",
series = "IEEE Wireless Communications and Networking Conference, WCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings",
address = "United States",
}