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Optimized Non-Uniform Pilot Pattern for OFDM Sensing

Amir Bouziane, Huseyin Arslan

Abstract

Standard periodic pilot patterns in orthogonal frequency division multiplexing (OFDM) systems induce severe delay-domain grating lobes, compromising radar sensing. This paper proposes a two-stage framework to design non-periodic pilot patterns that minimize the peak sidelobe level (PSL) while strictly enforcing communication anchor constraints. We black solve this combinatorial problem using a low-complexity hybrid greedy-stochastic cyclic coordinate descent (SCCD) algorithm. This approach shatters cyclic periodicities to suppress deterministic grating lobes beneath the impassable data-to-pilot interference (DPI) noise floor. System-level evaluations demonstrate the performance of the proposed design in resolving the sensing-communication trade-off, showing improved range root mean square error (RMSE) without degrading the primary communication bit error rate (BER).

Optimized Non-Uniform Pilot Pattern for OFDM Sensing

Abstract

Standard periodic pilot patterns in orthogonal frequency division multiplexing (OFDM) systems induce severe delay-domain grating lobes, compromising radar sensing. This paper proposes a two-stage framework to design non-periodic pilot patterns that minimize the peak sidelobe level (PSL) while strictly enforcing communication anchor constraints. We black solve this combinatorial problem using a low-complexity hybrid greedy-stochastic cyclic coordinate descent (SCCD) algorithm. This approach shatters cyclic periodicities to suppress deterministic grating lobes beneath the impassable data-to-pilot interference (DPI) noise floor. System-level evaluations demonstrate the performance of the proposed design in resolving the sensing-communication trade-off, showing improved range root mean square error (RMSE) without degrading the primary communication bit error rate (BER).

Paper Structure

This paper contains 12 sections, 22 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Sensing Channel and Received Signal
  4. Matched-Filter Output and Ambiguity Function
  5. Communication Channel Model
  6. Peak Sidelobe Level Metric
  7. Difference-Set Representation of the Ambiguity Function
  8. Difference Multiplicity Function
  9. Ambiguity Function in Terms of Differences
  10. Stage 1: Constructive Sidelobe Minimization (CSM)
  11. Numerical Validation
  12. Conclusion

Figures (3)

  • Figure 1: BER versus SNR over a frequency-selective Rayleigh fading channel.
  • Figure 2: Range RMSE versus SNR.
  • Figure 3: Theoretical vs. Operational PSL Suppression Gain ($\Delta$PSL) evaluation across varying pilot densities and anchor constraints.