用信息论方法提升无线通信中深度学习的抗噪能力
An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
- 引入最小误差熵损失函数，利用高阶统计特性增强抗干扰性
- 在多种信道下性能优于MSE和MAE，精度提升超20%，收敛更快
- 计算更轻量，适合实际无线通信场景，尤其适合高噪声环境
本文将最小误差熵（MEE）准则引入无线通信中的深度学习应用，作为一种先进的信息论损失函数。该准则利用高阶统计特性，在瑞利衰落和脉冲干扰等噪声环境下表现出更强的鲁棒性。同时，提出一种计算复杂度更低的MEE变体，以提升其在无线通信中的实用性。通过在空对空回归和室内定位两个关键任务上的仿真评估，结果表明MEE准则显著优于传统损失函数（如均方误差MSE和平均绝对误差MAE），在不同信道条件下实现超过20%的精度提升，并加快收敛速度。本工作证实MEE是深度学习模型在无线通信任务中的有力替代方案，具备更强的适应性和抗干扰能力。
原文摘要 · Abstract (English)
This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical properties, offering robustness in noisy scenarios like Rayleigh fading and impulsive interference. In addition, we propose a less computationally complex version of the MEE function to enhance practical usability in wireless communications. The method is evaluated through simulations on two critical applications: over-the-air regression and indoor localization. Results indicate that the MEE criterion outperforms conventional loss functions, such as mean squared error (MSE) and mean absolute error (MAE), achieving significant performance improvements in terms of accuracy, over $20 \%$ gain over traditional methods, and convergence speed across various channel conditions. This work establishes MEE as a promising alternative for wireless communication tasks in deep learning models, enabling better resilience and adaptability.
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