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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. [ABSTRACT FROM AUTHOR]