Large-scale wireless communication security information transmission method based on deep learning

By employing a deep learning-based end-to-end intelligent secure transmission method, combined with deep neural networks and dynamic phase beamforming, the robustness and security issues of existing secure wireless communication transmission methods in complex dynamic scenarios are addressed. This approach achieves efficient signal transmission and anti-interference capabilities, thereby enhancing the security protection level of communication networks.

CN122458016APending Publication Date: 2026-07-24SHENZHEN TONGHENG WEICHUANG TECH CO LTD
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Patent Information

Application Number
CN202610876776.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-17
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing secure transmission methods for large-scale wireless communication are difficult to achieve end-to-end joint optimization in complex dynamic scenarios. Their security mechanisms are singular, unable to adapt to time-varying channels, and vulnerable to eavesdropping and attacks. The accuracy of channel distortion compensation is limited, and traditional signal processing methods have poor robustness, making it impossible to balance high-quality transmission with high-level physical layer security protection.

Method used

An end-to-end intelligent secure transmission method based on deep learning is adopted. It utilizes deep neural networks at the transmitting and receiving ends for joint coding modulation and random secure scrambling, combined with dynamic phase beamforming. At the receiving end, signal separation and correction are performed through multi-layer convolution and LSTM iterative channel compensation. Adversarial training techniques are introduced to enhance robustness.

Benefits of technology

It achieves efficient signal transmission and anti-interference capabilities in complex dynamic scenarios, accurately separates signals and noise, improves the model's robustness against eavesdropping and attacks, supports adaptive channel parameter adjustment, and balances communication transmission efficiency with security protection.

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Abstract

The invention discloses a large-scale wireless communication security information transmission method based on deep learning, which relates to the field of deep learning, and comprises the following steps: a sending end completes joint coding modulation through a deep network according to an original bit and a channel state, fuses a security scrambling code, generates a specific constellation signal, and directionally transmits the specific constellation signal through beam forming; and a receiving end performs multi-scale feature extraction, channel compensation and interference elimination by using a deep network containing convolutional attention and LSTM, and finally performs sliding window joint detection decoding through a residual network to recover original information. The sending and receiving networks are subjected to alternate optimization and countermeasure training offline training in a simulation environment, so that the robustness and safety are improved. The method has the advantages that end-to-end intelligent secure transmission is realized, the transmission precision and anti-interference capability of large-scale wireless communication are guaranteed, eavesdropping, counterfeiting and man-in-the-middle attack are resisted through a multi-dimensional security mechanism, the method is adaptive to a large-scale MIMO communication scene, and both transmission reliability and physical layer security protection are considered.
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