# Low-Complexity Linear Equalization for OTFS Systems with Rectangular Waveforms

arXiv: https://arxiv.org/abs/1911.08133 · HTML: https://mlsanity.com/paper/1911.08133/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection](https://mlsanity.com/paper/2311.08543/) (2023)
- [Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection](https://mlsanity.com/paper/2406.16868/) (2024)

## Abstract (arXiv)

Orthogonal time frequency space (OTFS) is a promising technology for high-mobility wireless communications. However, the equalization realization in practical OTFS systems is a great challenge when the non-ideal rectangular waveforms are adopted. In this paper, first of all, we theoretically prove that the effective channel matrix under rectangular waveforms holds the block-circulant property and the special Fourier transform structure with time-domain channel. Then, by exploiting the proved property and structure, we further propose the corresponding low-complexity algorithms for two mainstream linear equalization methods, i.e., zero-forcing (ZF) and minimum mean square error (MMSE). Compared with the existing direct-matrix-inversion-based equalization, the complexities can be reduced by a few thousand times for ZF and a few hundred times for MMSE without any performance loss, when the numbers of symbols and subcarriers are both 32 in OTFS systems.