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Massive multiple-input multiple-output (mMIMO) communications are one of theenabling technologies of 5G and beyond networks. While prior work indicatesthat mMIMO networks employing time division duplexing have a significantcapacity growth potential, deploying mMIMO in frequency division duplexing(FDD) networks remains problematic. The two main difficulties in FDD networksare the scalability of the downlink reference signals and the overheadassociated with the required uplink feedback for channel state information(CSI) acquisition. To address these difficulties, most existing methods utilizeassumptions on the radio environment such as channel sparsity or angularreciprocity. In this work, we propose a novel cooperative method for a scalableand low-overhead approach to FDD mMIMO under the so-called grid-of-beamsarchitecture. The key idea behind our scheme lies in the exploitation of thenear-common signal propagation paths that are often found across several mobileusers located in nearby regions, through a coordination mechanism. In doing so,we leverage the recently specified device-to-device communications capabilityin 5G networks. Specifically, we design beam selection algorithms capable ofstriking a balance between CSI acquisition overhead and multi-user interferencemitigation. The selection exploits statistical information, through so-calledcovariance shaping. Simulation results demonstrate the effectiveness of theproposed algorithms, which prove particularly well-suited to rapidly-varyingchannels with short coherence time.

User Coordination for Fast Beam Training in FDD Multi-User Massive MIMO