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In this paper, we propose a novel method for efficient implementation of amassive Multiple-Input Multiple-Output (massive MIMO) system with FrequencyDivision Duplexing (FDD) operation. Our main objective is to reduce the largeoverhead incurred by Downlink (DL) common training and Uplink (UL) feedbackneeded to obtain channel state information (CSI) at the base station. Ourproposed scheme relies on the fact that the underlying angular distribution ofa channel vector, also known as the angular scattering function, is afrequency-invariant entity yielding a UL-DL reciprocity and has a limitedangular support. We estimate this support from UL CSI and interpolate it toobtain the corresponding angular support of the DL channel. Finally we exploitthe estimated support of the DL channel of all the users to design an efficientchannel probing and feedback scheme that maximizes the total spectralefficiency of the system. Our method is different from the existingcompressed-sensing (CS) based techniques in the literature. Using supportinformation helps reduce the feedback overhead from O(s*log M) in CS techniquesto O(s) in our proposed method, with $s$ and $M$ being sparsity order of thechannel vectors and the number of base station antennas, respectively.Furthermore, in order to control the channel sparsity and therefore the DLcommon training and UL feedback overhead, we introduce the novel concept ofactive channel sparsification. In brief, when the fixed pilot dimension is lessthan the required amount for reliable channel estimation, we introduce apre-beamforming matrix that artificially reduces the effective channeldimension of each user to be not larger than the DL pilot dimension, whilemaximizing both the number of served users and the number of probed angles. Weprovide numerical experiments to compare our method with the state-of-the-artCS technique.

FDD Massive MIMO: Efficient Downlink Probing and Uplink Feedback via Active Channel Sparsification