Abstract
Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove that the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods.
Original language | English |
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Pages (from-to) | 2227-2242 |
Number of pages | 16 |
Journal | IEEE Transactions on Wireless Communications |
Volume | 21 |
Issue number | 4 |
Early online date | 13 Sept 2021 |
DOIs | |
Publication status | Published - Apr 2022 |
Externally published | Yes |
Bibliographical note
This work was supported in part by the NSFC under Project 62101232.Keywords
- cross domain detection
- Orthogonal time frequency space
- performance analysis
- reduced-complexity detection
- state evolution