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|Abstract:||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.|
|Citation:||Xie, Xinqian, Mugen Peng, Wenbo Wang, and H. Vincent Poor. "Training design and channel estimation in uplink cloud radio access networks." IEEE Signal Processing Letters 22, no. 8 (2014): 1060-1064. doi:10.1109/LSP.2014.2380776|
|Pages:||1060 - 1064|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||IEEE Signal Processing Letters|
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