@inproceedings{20494b9f9b554c3f9c82aef1d7472bab,
title = "Compressive RF training and channel estimation in massive MIMO with limited RF chains",
abstract = "Recently, compressive channel estimation (CE) has been proposed to reduce the pilot overhead for massive MIMO with limited RF chains. One key issue is how to design the RF (analog) training vectors to achieve higher beamforming (BF) gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and exploit the channel support side information (CSSI) at the BS to do joint RF training and compressive CE. The narrow-beam RF training vectors are used to achieve a high BF gain, and the random RF training vectors are used to explore the unknown channel support to reduce the pilot overhead. Moreover, we derive closed-form bounds on the CE error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.",
keywords = "Compressive Channel Estimation, Hybrid Beamforming, Massive MIMO, RF Training",
author = "An Liu and Vincent Lau and Honig, {Michael L.} and Lixiang Lian",
note = "Funding Information: This work was partially supported by Project 61571383 supported by NSFC, and partially supported by RGC 16209916. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Communications, ICC 2017 ; Conference date: 21-05-2017 Through 25-05-2017",
year = "2017",
month = jul,
day = "28",
doi = "10.1109/ICC.2017.7997312",
language = "English (US)",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Merouane Debbah and David Gesbert and Abdelhamid Mellouk",
booktitle = "2017 IEEE International Conference on Communications, ICC 2017",
address = "United States",
}