Abstract
This letter describes a real-valued sparse Bayesian learning (SBL) approach for massive multiple-input multipleoutput (MIMO) downlink channel estimation. The main idea of the approach is to introduce a certain unitary transformation into pilots, so as to convert complex-valued channel recovery problems into real ones. Due to exploiting the real-valued structure of the data matrices, the new approach brings a significant decrease in computational complexity, as well as a good noise suppression. Simulation results demonstrate that the new method can reduce the computation load and improve the channel estimation performance simultaneously.
Highlights
M ASSIVE multiple-input multiple-output (MIMO) has become a key enabling technology for generation wireless communication systems
The channel state information at the transmitter (CSIT) in time division duplex (TDD) systems can be obtained by leveraging the channel reciprocity, but the reciprocity does not hold in frequency division duplex (FDD) systems
The FDD downlink channel estimation seems to be an extremely challenging task, as the training and feedback overhead is proportional to the number of antennas that can be quite large in massive multiple-input multipleoutput (MIMO) systems [1]
Summary
M ASSIVE multiple-input multiple-output (MIMO) has become a key enabling technology for generation wireless communication systems. The FDD downlink channel estimation seems to be an extremely challenging task, as the training and feedback overhead is proportional to the number of antennas that can be quite large in massive MIMO systems [1]. In order to solve this problem, Dai et al provided an off-grid sparse Bayesian learning (SBL) method for the downlink channel estimation [4]. We notice that a considerable amount of computations can be saved if we transform the complex-valued problem into a real one [5], [6]. We try to propose an efficient real-valued off-grid SBL approach for the massive MIMO downlink channel estimation in this letter. The main novelty of our approach is to introduce a certain unitary transformation into pilots, so as to convert complex-valued channel recovery problems into real ones. Simulation results reveal that the proposed method can simultaneously achieve lower complexity and better channel estimation performance than the state-of-the-art methods
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