SPARSE-OTFS-Net: A Sparse Robust OTFS Signal Detection Algorithm for 6G Ubiquitous Coverage

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread de...

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Veröffentlicht in:Electronics (Basel) Jg. 14; H. 17; S. 3532
Hauptverfasser: Ling, Yunzhi, Xu, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 04.09.2025
Schlagworte:
Accuracy
Adaptability
Algorithms
Artificial neural networks
Closed loops
Codes
Communication
Complexity
Concurrency
Connectivity
Contamination
Cramer-Rao bounds
Deep learning
Degradation
Error analysis
Error detection
High speed rail
Machine learning
Matched pursuit
Mean square errors
Neural networks
Noise reduction
Optimization
Pilots
Signal detection
Signal processing
Signal to noise ratio
Sparsity
Tracking errors
ISSN:2079-9292, 2079-9292
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Zusammenfassung:With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10−6 (7× improvement over single-Gaussian CNN’s 3.5 × 10−5). In 1024-user interference scenarios with BER = 10−5 requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14173532