The growing reliance on next generation wireless systems such as 5G and 6G demands highly secure and resilient communication frameworks that support latency-sensitive and high-throughput applications. A key enabler of these systems is the use of deep learning models for critical tasks including signal classification and modulation recognition. However, these models are vulnerable to wireless adversarial attacks, in which small, intentionally crafted perturbations added to normal communication cause model malfunction and further degrade network performance. To address these vulnerabilities, the project develops a framework that enhances the robustness of automatic modulation recognition under adversarial attacks in the next-generation wireless systems. The project's novelty is a bottom-up design from a communication pair to the whole network on addressing the fundamental limitations of deep learning models in adversarial wireless environments. The project's broader significance and importance are to improve the security of communication infrastructure that supports critical applications like autonomous transportation, industrial automation, and public safety. Additional contributions include the release of a large-scale wireless dataset for academic use, the integration of research outcomes into undergraduate and graduate curricula, and the engagement of students at all levels, including K-12, through interdisciplinary training and outreach activities. The research agend