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<p>Managing the Peak-to-Average Power Ratio (PAPR) in OFDM remains challenging due to unpredictable peak reformation caused by Power Amplifier (PA) distortion, pulse shaping, and multipath fading. Existing PAPR reduction methods fail to adapt dynamically, leading to spectral regrowth and the reintroduction of peaks. Additionally, modifications to reduce PAPR disrupt subcarrier orthogonality, increasing inter-symbol interference (ISI). A balance between PAPR reduction and ISI mitigation remains an unresolved trade-off in real-world OFDM deployments. Hence, this paper proposes an <i>SLM–Wavelet Volterra-Pre-Distortion Reinforced Equalization Network, implemented within a Reinforcement Recurrent Neural Network Learning (RRNNL) framework, to dynamically balance PAPR reduction and ISI mitigation</i> in OFDM systems. The first hidden layer employs Wavelet-Selective Partial Mapping to optimize phase adjustments and subcarrier allocation, minimizing peak power while preserving orthogonality. The second layer applies Volterra-Polynomial Guarded Pre-Distortion Filtering to correct PA distortion and mitigate ISI dynamically. The third layer incorporates Lagrange Multiplier and Zero-Forcing Equalization (ZFE) to optimize resource allocation and eliminate residual interference without reintroducing peak amplification. Experimental results demonstrate that the proposed method effectively balances PAPR reduction and ISI mitigation, achieving superior signal integrity and improved spectral efficiency compared to existing models.</p>

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Optimized PAPR reduction and ISI mitigation in OFDM systems using reinforcement SLM–wavelet volterra framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • M. N. Geetha,
  • M. N. Rekha,
  • Y. M. Raghavendra,
  • U. B. Mahadevaswamy

摘要

Managing the Peak-to-Average Power Ratio (PAPR) in OFDM remains challenging due to unpredictable peak reformation caused by Power Amplifier (PA) distortion, pulse shaping, and multipath fading. Existing PAPR reduction methods fail to adapt dynamically, leading to spectral regrowth and the reintroduction of peaks. Additionally, modifications to reduce PAPR disrupt subcarrier orthogonality, increasing inter-symbol interference (ISI). A balance between PAPR reduction and ISI mitigation remains an unresolved trade-off in real-world OFDM deployments. Hence, this paper proposes an SLM–Wavelet Volterra-Pre-Distortion Reinforced Equalization Network, implemented within a Reinforcement Recurrent Neural Network Learning (RRNNL) framework, to dynamically balance PAPR reduction and ISI mitigation in OFDM systems. The first hidden layer employs Wavelet-Selective Partial Mapping to optimize phase adjustments and subcarrier allocation, minimizing peak power while preserving orthogonality. The second layer applies Volterra-Polynomial Guarded Pre-Distortion Filtering to correct PA distortion and mitigate ISI dynamically. The third layer incorporates Lagrange Multiplier and Zero-Forcing Equalization (ZFE) to optimize resource allocation and eliminate residual interference without reintroducing peak amplification. Experimental results demonstrate that the proposed method effectively balances PAPR reduction and ISI mitigation, achieving superior signal integrity and improved spectral efficiency compared to existing models.