Massive MIMO Channel Estimation via Generalized Approximate Message Passing

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


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.


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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