NeTS: Medium: Collaborative Research:
Big Data Enabled Wireless Networking: A Deep Learning Approach

Reference #: CNS 1704092/1717315
Sponsor: NSF CNS Core Programs
PIs: Guoliang Xue (1714092), Jian Tang (1717315)
Duration: 08/15/2017 - 07/31/2020

Project Description: Wireless networks are becoming larger and more complicated, generating a huge amount of runtime statistics data (such as traffic load, resource usages, etc.) every second. Instead of treating big data in wireless networks as an unwanted burden, we aim to leverage them as a great opportunity for better understanding user demands and system capabilities such that we can optimize resource allocation to better serve mobile users. In addition, Cloud Radio Access Networks (C-RANs) have become a key enabling technology for the next generation wireless communication systems. Their centralized architecture makes it easy to collect and analyze various runtime system data. This project aims to exploit how the powerful new machine learning techniques, including Deep Learning (DL) and Deep Reinforcement Learning (DRL), can be leveraged to grasp the exciting opportunity provided by big data to enable future wireless networks to better serve their users. The proposed research is expected to significantly improve resource utilization of wireless networks and reduce their operational costs (such as power consumption), which can substantially benefit wireless network carriers and mobile users, and more importantly, is good for global environment. Beyond wireless networking, the proposed DL models and algorithms may find its applications in a large variety of domains, including video content analysis, user behavior study, etc. Moreover, the proposed project is expected to advance public understanding of the emerging 5G wireless communications, DL and DRL via publications, seminars and workshops, and international and industrial collaborations. The objective of this project is to develop a novel deep learning approach to enable efficient design and operations of future wireless networks with big data. Specifically, we will propose DL models and algorithms for spatiotemporal analysis and prediction of key system parameters, which can provide accurate and useful input information for existing resource allocation algorithms to better operate a wireless network. Moreover, we will develop a novel DRL-based control framework for a wireless network to efficiently allocate its resources by jointly learning the system environment and making decisions under the guidance of a powerful deep neural network. To achieve the above object, the project is organized into three cohesive thrusts: Thrust 1 Deep Learning based Modeling and Prediction; Thrust 2 Deep Reinforcement Learning based Dynamic Resource Allocation; and Thrust 3 Validation and Performance Evaluation.


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