  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 heads 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, where the basis-expansion-model
coefficients of access links (ALs) and the channel fading of wireless backhaul
links are first obtained, after which the time-domain channel samples of ALs
are restored in terms of maximizing the average effective signal-to-noise ratio
(AESNR). Simulation results show that the proposed channel estimation algorithm
can effectively decrease the estimation mean square error and increase the
AESNR in C-RANs, thus significantly outperforming the existing solutions.
