Training design and channel estimation in uplink cloud radio access networks

Xinqian Xie, Mugen Peng, Wenbo Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.

Original languageEnglish (US)
Article number6985582
Pages (from-to)1060-1064
Number of pages5
JournalIEEE Signal Processing Letters
Issue number8
StatePublished - Aug 1 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Channel estimation
  • cloud radio access networks


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