Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation
Abstract
Accurate channel estimation in orthogonal time frequency space (OTFS) systems with massive multiple-input multiple-output (MIMO) configurations is challenging due to high-dimensional sparse representation (SR). Existing methods often face performance degradation and/or high computational complexity. To address these issues and exploit intricate channel sparsity structure, this letter first leverages a novel hybrid burst-sparsity prior to capture the burst/common sparse structure in the angle/delay domain, and then utilizes an independent variational Bayesian inference (VBI) factorization technique to efficiently solve the high-dimensional SR problem. Additionally, an angle/Doppler refinement approach is incorporated into the proposed method to automatically mitigate off-grid mismatches.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2024
- DOI:
- 10.48550/arXiv.2408.12239
- arXiv:
- arXiv:2408.12239
- Bibcode:
- 2024arXiv240812239M
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- 7 pages, 3 figures