MAFENN: Multi-Agent Feedback Enabled Neural Network for Wireless Channel Equalization

IEEE Global Communications Conference (GLOBECOM), 2022

Recommended citation: Y. Li et al., "MAFENN: Multi-Agent Feedback Enabled Neural Network for Wireless Channel Equalization," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685522. https://ieeexplore.ieee.org/document/9685522

(ChatGPT-Generated) The paper proposes a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer for wireless channel equalization, which consists of a specific learnable feedback agent and two feed-forward agents that improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. The MAFENN framework is formulated into a three-player Stackelberg Game, which helps to optimize and train the model more efficiently, and experimental results show that the proposed method outperforms other equalizers in terms of symbol error rate (SER) performance at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.

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Recommended citation: Y. Li et al., “MAFENN: Multi-Agent Feedback Enabled Neural Network for Wireless Channel Equalization,” 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685522.