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<p>Waveforms with desirable ambiguity function properties are essential in active sensing and communication systems. This work presents a low-complexity iterative sequential algorithm for designing waveforms with reduced weighted merit factor (WMF) over Doppler frequency ranges of practical interest. To address the inherent non-convexity of the original problem, the proposed approach decomposes it into two simpler quadratic subproblems, which are efficiently solved using approximated closed-form solutions derived from a Taylor expansion-based approximation. This ensures both computational efficiency and practical feasibility. The algorithm leverages the computational efficiency of the fast Fourier transform (FFT) and seamlessly incorporates peak-to-average power ratio (PAPR) constraints, making it well-suited for scenarios requiring strict PAPR limitations. Numerical simulations illustrate that the proposed algorithm achieves a significantly faster convergence rate compared to conventional methods across diverse scenarios, underscoring its robustness, adaptability, and practical applicability.</p>

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A Low-complexity Algorithm for Designing Waveforms with Low Weighted Integrated Sidelobe Levels

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Xiaoming Dai,
  • Zhenyue Huang,
  • Yuquan Luo,
  • Xinya Liu,
  • Hua Li,
  • Xiyuan Wang

摘要

Waveforms with desirable ambiguity function properties are essential in active sensing and communication systems. This work presents a low-complexity iterative sequential algorithm for designing waveforms with reduced weighted merit factor (WMF) over Doppler frequency ranges of practical interest. To address the inherent non-convexity of the original problem, the proposed approach decomposes it into two simpler quadratic subproblems, which are efficiently solved using approximated closed-form solutions derived from a Taylor expansion-based approximation. This ensures both computational efficiency and practical feasibility. The algorithm leverages the computational efficiency of the fast Fourier transform (FFT) and seamlessly incorporates peak-to-average power ratio (PAPR) constraints, making it well-suited for scenarios requiring strict PAPR limitations. Numerical simulations illustrate that the proposed algorithm achieves a significantly faster convergence rate compared to conventional methods across diverse scenarios, underscoring its robustness, adaptability, and practical applicability.