To decrease the training overhead and improve the channel estimation accuracyin uplink cloud radio access networks (C-RANs), a superimposed-segment trainingdesign is proposed. The core idea of the proposal is that each mobile stationsuperimposes a periodic training sequence on the data signal, and each remoteradio heads prepends a separate pilot to the received signal before forwardingit to the centralized base band unit pool. Moreover, a complex-exponentialbasis-expansion-model based channel estimation algorithm to maximize aposteriori probability is developed, where the basis-expansion-modelcoefficients of access links (ALs) and the channel fading of wireless backhaullinks are first obtained, after which the time-domain channel samples of ALsare restored in terms of maximizing the average effective signal-to-noise ratio(AESNR). Simulation results show that the proposed channel estimation algorithmcan effectively decrease the estimation mean square error and increase theAESNR in C-RANs, thus significantly outperforming the existing solutions.
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