Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

TL;DR

This paper introduces a superimposed-segment training design and a complex-exponential basis-expansion-model based channel estimation algorithm for uplink C-RANs, significantly reducing training overhead and improving estimation accuracy.

Contribution

It proposes a novel training scheme combined with an advanced channel estimation algorithm tailored for uplink C-RANs, enhancing performance over existing methods.

Findings

Reduced channel estimation mean square error

Increased average effective SNR (AESNR)

Outperforms existing solutions in simulations

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 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.