CCRI: Medium: Collaborative Research: 3DML: A Platform for Data, Design and Deployed Validation of Machine Learning for Wireless Networks and Mobile Applications

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The ever-increasing complexity of wireless networks and their emerging novel user applications (such as autonomous cars, virtual reality, and e-health) have spurred a significant demand to develop machine learning (ML) empowered, intelligent network management and optimization. However, there are still two main barriers to unleashing such innovations: (i) ML-based approaches require many large labeled datasets, which are difficult to acquire in the wireless context due to both privacy and cost challenges; and (ii) the challenge of deploying complex ML models into resource-constrained wireless devices. This project’s overarching goal is to design, develop, and disseminate a community platform called 3DML, for facilitating the development of ML-based innovations for next-generation wireless networks and mobile applications. 3DML will be the first platform, designed from the ground up, to meet the urgent need of exploring ML-based innovations for wireless applications, featuring three integrated key components. First, this project will develop 3DML-Data which has the ability to operate in networks with different scales and capture diverse network operating states and enable the collection of unprecedentedly diverse labeled datasets. Second, this project will design 3DML-Client, which consists of automated tools and compression libraries to (i) automatically generate efficient ML models and deployment strategies for achieving optimal trade-offs between task performance and resource consumption given diverse devices and applications, and (ii) provide a comprehensive pool of efficient ML modules and functions for fast development. Third, this project will develop 3DML-Infrastructure, which can make use of 3DML-Client with data collected from 3DML-Data, to generate efficient ML algorithms deployed into wireless infrastructure, and include a methodology for researchers to use ML algorithms to customize key modules for massive MIMO channel estimation, detection, decoding, beamforming, and spectrum sharing. This project addresses a pressing need of the wireless research community to develop a platform for ML-empowered intelligent network management. The success of this project will provide data and tools to enable automated and self-customized exploration and deployment of ML-based approaches for wireless applications. The educational program with workshops, online courses, and internships will involve not only undergraduate and graduate students from various institutes, but also practitioners from industry. Overall, 3DML will open up a host of new possibilities for developing innovations towards next generation intelligent wireless networks, including enhanced mobile broadband, massive Internet-of-things and ultra-low-latency applications in order to support numerous emerging applications. All of the developed datasets, tools, and libraries will be released at https://3dml.rice.eduThis 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.