The paper proposes a novel rank-1 subspace channel estimator for massive MIMO systems. The key highlights are:
The estimator first acquires highly accurate angle-of-arrival (AoA) information by leveraging a constructed space-embedding Hankel matrix and the rank-1 subspace method. This breaks the resolution limit of the classical FFT-based method.
It then adopts a post-reception beamforming scheme to estimate the unbiased channel gains, relying on the maximum likelihood (ML) criterion.
Theoretical analysis shows that the extra gain achieved by the proposed estimator over the linear MMSE estimator grows according to the rule of O(log10 M), where M is the number of antennas at the base station (BS).
A fast implementation is further designed by utilizing the inherent low-rank property of the Hankel matrix, which reduces the computational complexity from O(M^3) to O(KP^2M), where K is the number of users and P is the number of paths (P << M).
Numerical simulations validate the theoretical results, demonstrating that the proposed estimator substantially outperforms the linear MMSE estimator and sparsity-based methods in accuracy, while dramatically reducing the computational complexity by 2~3 orders of magnitude.
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by Bin Li,Zipin... lúc arxiv.org 04-23-2024
https://arxiv.org/pdf/2404.13603.pdfYêu cầu sâu hơn