MLWiNS: Decentralized Heterogeneous Deep Learning for Efficient Wireless Spectrum Monitoring

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As wireless networks evolve to be increasingly massive and complex, traditional spectrum monitoring methods with model-based signal processing techniques have become inadequate and may even fail to provide accurate wireless network evaluation. Meanwhile, deep learning techniques have been proven successful in standard centralized learning tasks (e.g., image classification), yet it is barely explored for large-scale wireless sensing systems, which entail unconventional node distribution, complex channel fading and user collaboration opportunities. This project develops innovative decentralized heterogeneous deep learning techniques for large-scale wireless systems. The outcomes of this project lead to technical innovations that tackle several major challenges of the state-of-the-art wireless sensing and management systems, including the incapability of conventional sensing and management schemes in ultra-wide wireless spectrum settings, the difficulty in handling heterogeneous tasks and non-IID data with deep learning technologies, as well as the costly overhead of communication and computation in distributed deep learning for large-scale networks.This project addresses the unique challenges of large-scale wireless spectrum sensing by developing a revolutionary decentralized deep learning framework. Three main thrusts are planned. In Thrust 1, major challenges of complex and large-scale wireless spectrum sensing nowadays are investigated, and an innovative deep learning-based solution is developed for practical spectrum sensing tasks. In Thrust 2, dedicated communication and computation schemes are developed to optimize the performance of the proposed decentralized deep learning framework. In Thrust 3, the very first exploratory effort is made to understand and utilize the intricate role of machine learning in spectrum management, based on the key observation that it consumes wireless network resources to bring in added value to network resource utilization. Experimental testing is demonstrated for practical spectrum monitoring applications. The proposed wireless sensing and management system can benefit a plethora of large-scale wireless network systems, such as a 5G wireless network and other large-scale mesh networking systems. The education plan enhances existing curricula and pedagogy by integrating interdisciplinary modules on embedded systems, mobile computing, and machine learning with newly developed teaching practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.