Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

Provided by: Beijing University of Posts and Telecommunications
Topic: Cloud
Format: PDF
Cloud Radio Access Networks (C-RANs) have received considerable research interest as one of the most promising solutions to mitigate interference, fulfill energy efficiency demands and support high-rate transmission in the fifth generation cellular network. To decrease the training overhead and improve the channel estimation accuracy under time-varying environments in uplink Cloud Radio Access Networks (C-RANs), a superimposed-segment training design is proposed whose core idea is that each mobile station puts a periodic training sequence on the top of the data signal, and Remote Radio Heads (RRHs) insert a separate pilot prior to the received signal before forwarding to the centralized Base Band Unit (BBU) pool.

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