Deep Learning Based Channel Estimation in Fog Radio Access Networks

As a promising paradigm of the fifth generation networks, fog radio access network (F-RAN) has attracted lots of attention nowadays. To fully utilize the promising gain of F-RANs, the acquisition of accurate channel state information is significant. However, conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead. In this paper, we consider the channel estimation in F-RANs with fog access point (F-AP) equipped with massive antennas. Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain, Gated Recurrent Unit (GRU), a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead. The GRU-based method can capture the hidden sparsity structure automatically through the network training. Moreover, to further improve the channel estimation, a bidirectional GRU based method is proposed, whose target channel structure is decided by previous and subsequent structures. We compare the performance of our proposed channel estimation with traditional methods (Orthogonal Matching Pursuit (OMP) and Simultaneous OMP (SOMP)). Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.