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In this work, we consider estimating user positions in a spatiallydistributed antenna system (DAS) from the uplink channel state information(CSI). However, with the increased number of remote radio heads (RRHs),collecting CSI at a central unit (CU) can significantly increase the fronthauloverhead and computational complexity of the CU. This problem can be mitigatedby selecting a subset of RRHs. Thus, we present a deep learning-based approachto select a subset of RRHs for wireless localization. We employ an RRHselection layer that is jointly trained with the rest of the network and learnthe model parameters as well as the set of selected RRHs. We show that theselection strategy comes at a relatively small cost of localizationperformance. Nonetheless, by comparison to a trivial approach based on themaximization of the channel gain, we show that the proposed method leads tosignificant performance gains in a propagation environment dominated bynon-line-of-sight.

Learning-based Remote Radio Head Selection and Localization in Distributed Antenna System