Doubly-Iterative Sparsified MMSE Turbo Equalization for OTFS Modulation
Haotian Li, Qiyue Yu

TL;DR
This paper introduces a novel doubly-iterative sparsified MMSE turbo equalizer for OTFS modulation, leveraging sparsity and iterative algorithms to achieve near-optimal performance with reduced complexity in high-mobility scenarios.
Contribution
It proposes a new equalizer that exploits OTFS channel sparsity and combines GMRES and FSPAI algorithms for efficient, near-optimal turbo equalization.
Findings
Achieves near-MAP performance with only 0.2 dB loss at BER of 10^{-4}
Maintains linear complexity in equalization process
Demonstrates effective sparsification with minimal performance degradation
Abstract
Currently, orthogonal time frequency space (OTFS) modulation has drawn much attention to reliable communications in high-mobility scenarios. This paper proposes a doubly-iterative sparsified minimum mean square error (DI-S-MMSE) turbo equalizer, which iteratively exchanges the extrinsic information between a soft-input-soft-input (SISO) MMSE estimator and a SISO decoder. Our proposed equalizer does not suffer from short loops and approaches the performance of the near-optimal symbol-wise maximum a posteriori (MAP) algorithm. To exploit the inherent sparsity of OTFS system, we resort to graph theory to investigate the sparsity pattern of the channel matrix, and propose two sparsification guidelines to reduce the complexity of calculating the matrix inverse at the MMSE estimator. Then, we apply two iterative algorithms to MMSE estimation, i.e., the Generalized Minimal Residual (GMRES) and…
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Taxonomy
TopicsPAPR reduction in OFDM · Optical Network Technologies · Advanced Power Amplifier Design
