Cross-technology interference sources poses a great challenge for improving throughputs of wireless local area networks (WLAN) and wireless interference signal recognition (WISR) can provide a precondition for mitigating this problem. The quadruple generative adversarial network (QGAN) has shown its prevailing performance for specific emitter identification (SEI). In this paper, an enhanced collaborative learning mechanism is proposed to improve QGAN's performance for WISR. ACGAN is involved in the Improved-QGAN architecture to substitute original GAN, and loss functions are further optimized for generative, representation and classification sub-networks. Besides, a lightweight model based on knowledge distillation (KD) is presented to reduce memory consumption and computational complexity at inference phase. Numerical results indicate that the proposed Improved-QGAN outperforms the other baseline algorithms both on the experimental dataset and benchmark dataset.

跨技术干扰源对提高无线局域网 (WLAN) 的吞吐量提出了巨大挑战，而无线干扰信号识别 (WISR) 可以为缓解这一问题提供前提条件。四元生成对抗网络 (QGAN) 已显示出其在特定发射器识别 (SEI) 方面的主要性能。在本文中，提出了一种增强的协作学习机制来提高 QGAN 在 WISR 中的性能。ACGAN 参与了改进的 QGAN 架构以替代原始 GAN，并针对生成、表示和分类子网络进一步优化损失函数。此外，提出了一种基于知识蒸馏（KD）的轻量级模型，以减少推理阶段的内存消耗和计算复杂度。