Integrated CSI Feedback and Localization using Deep Learning

Deep learning (DL) has shown great potential in channel state information (CSI) feedback and localization. In this paper, a DL-based integrated CSI feedback and localization framework called FLnet, in which the feedback and localization tasks complement each other, is proposed. Specifically, unlike the existing works that sequentially realize the above two tasks, FLnet jointly designs the autoencoder-based feedback and deep neural networks (DNN)-based localization tasks. The encoder at the user equipment (UE) compresses and quantizes the downlink CSI. Then, the decoder and the DNN at the base station reconstruct the downlink CSI and predict the location of the UE based on the feedback information, respectively. The feedback and localization modules are trained together by an end-to-end approach. Simulation results show that the localization error of FLnet is reduced by 30% compared with that of the separate design while the feedback performance is comparable or even improved.