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Channel state information (CSI) acquisition is a significant bottleneck inthe design of Massive MIMO wireless systems, due to the length of the trainingsequences required to distinguish the antennas (in the downlink) and the users(for the uplink where a given spectral resource can be shared by a large numberof users). In this article, we focus on the downlink CSI estimation case.Considering the presence of spatial correlation at the base transceiver station(BTS) side, and assuming that the per-user channel statistics are known, weseek to exploit this correlation to minimize the length of the pilot sequences.We introduce a scheme relying on non-orthogonal pilot sequences and feedbackfrom the user terminal (UT), which enables the BTS to estimate all downlinkchannels. Thanks to the relaxed orthogonality assumption on the pilots, thelength of the obtained pilot sequences can be strictly lower than the number ofantennas at the BTS, while the CSI estimation error is kept arbitrarily small.We introduce two algorithms to dynamically design the required pilot sequences,analyze and validate the performance of the proposed CSI estimation methodthrough numerical simulations using a realistic scenario based on the one-ringchannel model.

SNOPS: Short Non-Orthogonal Pilot Sequences for Downlink Channel State Estimation in FDD Massive MIMO