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<p>In contemporary communication systems, reliable data transmission depends on reducing the negative impacts of nonlinearities and inter-symbol interference (ISI) in communication channels. In this study, we introduce a hybrid channel equalization approach that integrates the temporal modeling capability of Long Short-Term Memory (LSTM) networks with the robust classification ability of Support Vector Machines (SVM) to address nonlinear channel distortions effectively. The LSTM model is used because it can manage the time-varying features of channel impairments since it can learn and represent temporal relationships in sequential data. By mapping the learnt feature representations to the original transmitted symbols, the SVM classifier then improves the output of the LSTM and produces reliable decision boundaries in high-dimensional feature spaces. The suggested LSTM-SVM equalizer is trained using simulated datasets that include multipath fading and additive noise and are produced from nonlinear channel models. The effectiveness of the proposed system is evaluated in terms of Mean Squared Error (MSE) and Bit Error Rate (BER) across a range of signal-to-noise ratio (SNR) conditions. The simulation outcomes reveal that the proposed LSTM–SVM equalizer consistently outperforms standalone LSTM and SVM-based equalizers. A viable solution for equalization in nonlinear communication channels is provided by this hybrid technique, which successfully blends the strong classification capability of SVM with the temporal learning capabilities of LSTM.</p>

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Advanced Nonlinear Channel Equalization Using LSTM-SVM: A Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Vundavalli Ravindra,
  • Pradyumna Kumar Mohapatra,
  • Ravi Narayan Panda,
  • Saroja Kumar Rout,
  • Rojalin Samantaray

摘要

In contemporary communication systems, reliable data transmission depends on reducing the negative impacts of nonlinearities and inter-symbol interference (ISI) in communication channels. In this study, we introduce a hybrid channel equalization approach that integrates the temporal modeling capability of Long Short-Term Memory (LSTM) networks with the robust classification ability of Support Vector Machines (SVM) to address nonlinear channel distortions effectively. The LSTM model is used because it can manage the time-varying features of channel impairments since it can learn and represent temporal relationships in sequential data. By mapping the learnt feature representations to the original transmitted symbols, the SVM classifier then improves the output of the LSTM and produces reliable decision boundaries in high-dimensional feature spaces. The suggested LSTM-SVM equalizer is trained using simulated datasets that include multipath fading and additive noise and are produced from nonlinear channel models. The effectiveness of the proposed system is evaluated in terms of Mean Squared Error (MSE) and Bit Error Rate (BER) across a range of signal-to-noise ratio (SNR) conditions. The simulation outcomes reveal that the proposed LSTM–SVM equalizer consistently outperforms standalone LSTM and SVM-based equalizers. A viable solution for equalization in nonlinear communication channels is provided by this hybrid technique, which successfully blends the strong classification capability of SVM with the temporal learning capabilities of LSTM.