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A Structured Sparse Bayesian Channel Estimation Approach for Orthogonal Time-Frequency Space Modulation.

Mi Zhang1, Xiaochen Xia1, Kui Xu1

  • 1School of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
Summary
This summary is machine-generated.

Orthogonal time-frequency space (OTFS) modulation aids integrated sensing and communication (ISAC). This study introduces a Bayesian learning approach for accurate channel estimation in OTFS-ISAC systems, improving performance in low SNR conditions.

Keywords:
channel estimationintegrated sensing and communicationorthogonal time–frequency space modulationstructured sparse Bayesian learning

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Area of Science:

  • Wireless communications
  • Signal processing
  • Integrated sensing and communication (ISAC)

Background:

  • Orthogonal time-frequency space (OTFS) modulation is suitable for high-mobility scenarios and integrated sensing and communication (ISAC).
  • Accurate channel acquisition is crucial for OTFS-ISAC systems but is challenging due to fractional Doppler shifts.
  • Existing methods struggle with efficient channel estimation in OTFS-based ISAC.

Purpose of the Study:

  • To develop an accurate and efficient channel estimation method for OTFS-ISAC systems.
  • To address the challenges posed by fractional Doppler frequency shifts in OTFS signals.
  • To improve the performance of ISAC systems, particularly in low signal-to-noise ratio (SNR) environments.

Main Methods:

  • Derivation of the sparse structure of the channel in the delay-Doppler (DD) domain for OTFS signals.
  • Proposal of a structured Bayesian learning approach for channel estimation.
  • Development of a successive majorization-minimization (SMM) algorithm for posterior channel estimate computation.

Main Results:

  • The proposed structured Bayesian learning approach accurately estimates the delay-Doppler channel.
  • The SMM algorithm efficiently computes the posterior channel estimates.
  • The new method significantly outperforms existing schemes, especially at low SNR.

Conclusions:

  • The proposed structured Bayesian learning approach is effective for channel estimation in OTFS-ISAC systems.
  • The method demonstrates superior performance compared to reference schemes, particularly in challenging low SNR conditions.
  • This work contributes to the advancement of reliable and efficient ISAC systems utilizing OTFS modulation.