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<p>In the rapidly evolving landscape of beyond 5G (B5G) networks, ensuring robust security is paramount due to the increased complexity and heterogeneity of network architectures. This article presents a comprehensive security analysis using a Bi-LSTM-based intelligent deep learning method to address the emerging security challenges in B5G networks. Leveraging the bidirectional long short-term memory (Bi-LSTM) architecture, our approach effectively captures temporal dependencies and context from sequential data, enhancing the detection and mitigation of sophisticated cyber threats. The proposed method integrates feature extraction and classification into a unified framework, facilitating real-time analysis of the throughput of the framework. By analyzing various parameters such as bit error rate (BER), peak power and power spectral density (PSD), the projected Bi-LSTM model demonstrates superior performance compared to traditional. The study further explores the limitation and adaptability of the Bi-LSTM approach across diverse network scenarios, including ultra-dense networks and massive IoT deployments. Our findings underscore the critical role of advanced deep learning models in fortifying B5G networks, highlighting their potential to evolve in tandem with future network advancements.</p>

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Signal Detection Analysis in Beyond 5G Networks Using Intelligent Bi-LSTM-Based Deep Learning Method

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Arun Kumar,
  • Nishant Gaur,
  • Aziz Nanthaamornphong

摘要

In the rapidly evolving landscape of beyond 5G (B5G) networks, ensuring robust security is paramount due to the increased complexity and heterogeneity of network architectures. This article presents a comprehensive security analysis using a Bi-LSTM-based intelligent deep learning method to address the emerging security challenges in B5G networks. Leveraging the bidirectional long short-term memory (Bi-LSTM) architecture, our approach effectively captures temporal dependencies and context from sequential data, enhancing the detection and mitigation of sophisticated cyber threats. The proposed method integrates feature extraction and classification into a unified framework, facilitating real-time analysis of the throughput of the framework. By analyzing various parameters such as bit error rate (BER), peak power and power spectral density (PSD), the projected Bi-LSTM model demonstrates superior performance compared to traditional. The study further explores the limitation and adaptability of the Bi-LSTM approach across diverse network scenarios, including ultra-dense networks and massive IoT deployments. Our findings underscore the critical role of advanced deep learning models in fortifying B5G networks, highlighting their potential to evolve in tandem with future network advancements.