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Enhanced Channel Estimation for RIS-Assisted OTFS Systems by Introducing ELM Network.

Mintao Zhang1, Zhiying Liu1, Li Wang1

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

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|September 19, 2025
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Summary
This summary is machine-generated.

This study introduces Extreme Learning Machine (ELM) for channel estimation (CE) in reconfigurable intelligent surface (RIS)-assisted Orthogonal Time Frequency Space (OTFS) systems. The method enhances symbol detection performance, even with varying communication parameters.

Keywords:
channel estimationextreme learning machineorthogonal time frequency spacereconfigurable intelligent surfacessymbol detection

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Machine Learning

Background:

  • Reconfigurable Intelligent Surfaces (RIS) enhance Orthogonal Time Frequency Space (OTFS) systems in high-mobility scenarios.
  • Integrating RIS into OTFS systems significantly increases channel estimation (CE) complexity.
  • Machine Learning (ML) offers potential for reducing CE complexity, but ML-based CE in RIS-assisted OTFS is under-explored.

Purpose of the Study:

  • To address the complexity of channel estimation (CE) in RIS-assisted OTFS systems using ML.
  • To investigate the effectiveness of Extreme Learning Machine (ELM) for improving CE accuracy in these systems.
  • To enhance the learning ability of ELM by incorporating a threshold-based initial feature extraction method.

Main Methods:

  • Proposed an Extreme Learning Machine (ELM) based channel estimation (CE) method for RIS-assisted OTFS systems.
  • Incorporated a threshold-based approach for initial feature extraction to overcome ELM's parameter limitations.
  • Utilized a message passing algorithm for data symbol detection (SD).

Main Results:

  • The proposed ELM method significantly improves channel estimation (CE) accuracy in RIS-assisted OTFS systems.
  • Simulation results demonstrate enhanced symbol detection (SD) performance compared to existing methods.
  • The method shows robustness against changes in modulation order, maximum velocity, and number of sub-surfaces.

Conclusions:

  • The ELM-based approach with initial feature extraction is effective for channel estimation (CE) in RIS-assisted OTFS systems.
  • This method improves symbol detection (SD) performance and offers robustness in high-mobility communication.
  • The study bridges a gap in ML-based CE for RIS-assisted OTFS, paving the way for intelligent applications.