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.
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Li, M., Han, X., Zhang, W., Zhang, S. (2020). Massive MIMO Channel Estimation via Generalized Approximate Message Passing. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_18
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DOI: https://doi.org/10.1007/978-981-13-9409-6_18
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