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Massive MIMO Channel Estimation via Generalized Approximate Message Passing

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)

Abstract

In this paper, we proposed a channel estimation scheme for an off-grid massive MIMO channel model, with the consideration of carrier frequency offset at the BS antenna array. We first developed an off-grid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the off-grid bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme.

Keywords

Massive MIMO Off-grid Carrier frequency offset Sparse Bayesian learning GAMP 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Xidian UniversityXi’anPeople’s Republic of China
  2. 2.Xi’an Jiaotong UniversityXi’anPeople’s Republic of China

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