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- W2612781504 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." @default.
- W2612781504 created "2017-05-19" @default.
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- W2612781504 date "2017-03-01" @default.
- W2612781504 modified "2023-09-25" @default.
- W2612781504 title "Cluster Formation with Data-Assisted Channel Estimation in Cloud-Radio Access Networks" @default.
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- W2612781504 doi "https://doi.org/10.1109/wcnc.2017.7925674" @default.
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