Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems

By Chongwen Huang, Lei Liu, Chau Yuen, Sumei Sun. 2016

159 citations recorded by the source corpus.

We propose an iterative channel estimation algorithm based on the least square estimation (LSE) and sparse message passing (SMP) algorithm for the millimeter wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli–Gaussian distribution and the channel matrix is sparse with only a few nonzero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of nonzero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cramér–Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations.

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “For example, Huang et al. [27] and Rappaport et al. [222] have fused raw signals that are divided through multiple antenna during transmission.”
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication (2018, unknown): “8 mW for 3 -, 4 -, 5 -, and 6 -bit resolution phase shifting [18], [42]–[44].”
Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO (2019, methodology): “, [12], [14], [15]) by turning off the LIM.”
Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field? (2021, methodology): “Correspondingly, the channel h far-ﬁeld m can be transformed into its angular-domain representation h A m by the spatial Fourier transform matrix [11], i.e., where F ∈ C N × N denotes the Fourier transform matrix.”