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Flag-Preamble-Based Delay-Doppler Channel Estimation for Next-Evolution Waveforms

Yuan Liu, Yilin Qi, Caiqin Li, Haoran Yin, Yanqun Tang, Yao Ge

Abstract

Accurate delay-Doppler channel estimation is critical for next-evolution waveforms (NEWs) to enable reliable signal detection. This paper proposes a robust channel estimation algorithm that integrates Flag sequences optimized via an adaptive accelerated parallel majorization-minimization (AP-MM) algorithm with a proposed channel estimation algorithm. To enable efficient, low-complexity parameter extraction and further overcome the robustness issues of conventional greedy estimation, we introduce two key enhancements, i.e., a candidate selection strategy to mitigate spurious sidelobe peaks, and a global least squares (LS) refinement stage to eliminate error propagation caused by sidelobe masking effects. Numerical results demonstrate that the proposed scheme significantly outperforms traditional existing algorithms, achieving the desired estimation accuracy.

Flag-Preamble-Based Delay-Doppler Channel Estimation for Next-Evolution Waveforms

Abstract

Accurate delay-Doppler channel estimation is critical for next-evolution waveforms (NEWs) to enable reliable signal detection. This paper proposes a robust channel estimation algorithm that integrates Flag sequences optimized via an adaptive accelerated parallel majorization-minimization (AP-MM) algorithm with a proposed channel estimation algorithm. To enable efficient, low-complexity parameter extraction and further overcome the robustness issues of conventional greedy estimation, we introduce two key enhancements, i.e., a candidate selection strategy to mitigate spurious sidelobe peaks, and a global least squares (LS) refinement stage to eliminate error propagation caused by sidelobe masking effects. Numerical results demonstrate that the proposed scheme significantly outperforms traditional existing algorithms, achieving the desired estimation accuracy.
Paper Structure (18 sections, 15 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 15 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. PRELIMINARIES AND SYSTEM MODEL
  3. Flag Sequence
  4. System Model
  5. TRADITIONAL AND PROPOSED FLAG BASED CHANNEL ESTIMATION
  6. Traditional Flag-based Channel Estimation Algorithm
  7. Proposed Flag-based Channel Estimation Algorithm
  8. Multi-Candidate Line Search
  9. Peak Search
  10. Simulation-based Parameter Optimization
  11. Global LS Refinement
  12. Complexity Analysis
  13. SIMULATION RESULTS AND DISCUSSION
  14. Simulation Parameters
  15. Parameters Selection for the Proposed Flag Algorithm
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Ambiguity function of traditional Flag sequence.
  • Figure 2: Frame structure.
  • Figure 3: Parameters selection for the proposed Flag algorithm: (a) Trade-off regarding candidate numbers $K$; (b) Trade-off regarding relative threshold $\gamma$.
  • Figure 4: MSE results for channel estimation algorithms.
  • Figure 5: PD and PM of proposed Flag algorithm.
  • ...and 1 more figures