Jian Tang

Research Projects

• Big Data Enabled Wireless Networking: A Deep Learning Approach (Project Website)
  Award #:1704662
  Sponsor: NSF CNS Core Program (Medium)
  PI: Jian Tang
  Co-PI: Yanzhi Wang
  Duration: 08/2017 - 07/2021
  Project Description: 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.
  
  
• Enabling High-Quality Mobile Crowdsourcing with Lifestyle-aware and Energy-efficient Control (Project Website)
  Award #:1525920
  Sponsor: NSF CNS Core Program
  PI: Jian Tang
  Duration: 10/2015 - 09/2018
  Project Description: The objective of this project to enable high-quality mobile crowdsourcing by
  designing a novel and holistic solution for crowd recruitment, which utilizes an energy-efficient
  framework for learning lifestyles of mobile users via smartphone sensing, and then employs
  lifestyle-aware algorithms and incentive mechanisms for crowd recruitment. To achieve the
  above objective, the proposed research is organized into three cohesive research thrusts: Thrust 1
  Energy-efficient Lifestyle Learning: a unified and energy-efficient framework will be developed to
  learn lifestyles of mobile users by characterizing their behaviors and habits, and predicting their future
  activities; Thrust 2 Lifestyle-aware Crowd Recruitment and Incentive Mechanisms: Quality of Crowd (QoC)
  models and lifestyle-aware algorithms will be developed for effective crowd recruitment; Thrust 3 Implementation
  and Performance Evaluation: the proposed approaches will be implemented on a smartphone sensing platform
  and will be validated and evaluated via extensive simulation and experiments.
  
  

• CogCloud: A Spectrum-Efficient and Green Cloud Platform for Radio-as-a-Service
  Over a Cognitive Radio Substrate (Project Website)
  Award #: 1443966
  Sponsor: NSF EARS Program
  PI: Jian Tang
  Co-PI: M. Cenk Gursoy
  Duration: 01/2015 - 12/2018
  Project Description: The objective of this project is to develop a spectrum-efficient
  cloud platform, namely CogCloud, to enable Radio-as-a-Service (RaaS) over a cognitive
  radio substrate, and minimize its energy consumption by designing a two-level closed-loop
  control framework that leverages cloud-level and BS-level optimization for coarse-grained
  and fine-grained control respectively over radio resources and wireless users. CogCloud is
  expected to have the following desirable properties: 1) RaaS for multiple independent Mobile
  Virtual Network Operators (MVNOs): Radio resources in a cognitive radio substrate are
  provided as a service to multiple independent MVNOs. 2) Performance isolation: Changes
  in an MVNO (such as the number of wireless users, their traffic load, etc) do not affect the
  performance of wireless users of other MVNOs. 3) Spectrum efficiency: Spectrum availability
  is fully exploited by cognitive radios and efficiently managed by the cloud to support QoS-enabled
  wireless communications. 4) Energy efficiency: The cognitive radio substrate and its radio resources
  are operated in the most energy-efficient way. To achieve these goals, the project is organized into
  four cohesive research thrusts: Thrust 1 System Architecture Design and Implementation,
  Thrust 2 Cloud-level Optimization, Thrust 3 BS-level Optimization for Fine-grained Control, and
  Thrust 4 Validation and Performance Evaluation. The project is expected to make a significant
  impact on the advancement of Cloud Computing, Cognitive Radio Networking and Green
  Communications, and advance public understanding of emerging research areas, such as
  Wireless Virtualization and Cloud-based Radio Access Networks (RANs), via publications,
  seminars, workshops and international collaborations. Special efforts are made to engage
  students from under-represented groups.
  
  
• A Green and Incentive Platform for Mobile Phone Sensing (Project Website)
  Award #: 1218203
  Sponsor: NSF CNS Core Program
  PI: Jian Tang
  Duration: 08/2012 - 07/2016
  Project Description: Sensors on mobile phones can enable attractive sensing
  applications in different domains such as environmental monitoring, social network,
  healthcare, etc. However, fundamental energy-efficient resource management
  problems have not been well studied for mobile phone sensing. In addition, how to
  provide incentives to attract user participation has not been well addressed. The
  objective of this project is to develop a unified and green platform for mobile phone
  sensing, optimize its performance by designing energy-efficient algorithms for sensing
  task management, and develop game-theoretic incentive mechanisms to attract
  user participation. The proposed research is organized into four cohesive research
  thrusts: 1) Design and implement a unified software architecture to enable support
  for various sensing applications. 2) Develop both platform-centric and user-centric
  incentive mechanisms to attract user participation. 3) Develop energy-efficient algorithms
  to manage sensing tasks. 4) Test the developed platform and algorithms via simulation
  and real experiments. This research will result in a unified and green mobile phone sensing
  system. Fundamental resource management problems will be solved by theoretically-sound
  and practical algorithms. The project will also result in novel incentive models and mechanisms
  for mobile phone sensing. In addition, the proposed platform can create a completely new
  type of online marketplace. The proposed energy-efficient algorithms can benefit both mobile
  users and the environment. The project is also expected to advance public understanding of
  mobile phone sensing via publications, seminars and workshops.
  

• Leveraging Smart Antennas for WiMAX-based Mesh Networking
  Award #: 1113398
  Sponsor: NSF CAREER Program
  PI: Jian Tang
  Duration: 02/2009 - 01/2015
  Project Description: The WiMAX technology (IEEE 802.16) can provide high-speed and
  long-range wireless communications for a large variety of applications. Smart antennas,
  such as Digital Adaptive Array (DAA) and Multiple Input and Multiple Output (MIMO) antennas,
  can offer a long transmission range and improve network capacity via interference suppression
  and spatial multiplexing. The objective of this project is to provide a comprehensive networking
  solution for a WiMAX mesh network with smart antennas by investigating the fundamental problems,
  including scheduling, routing and relay station placement. Specifically, their computational complexities
  will be investigated, and efficient and standard-compliant algorithms and protocols will be proposed
  to solve them. The PI will also develop a relay station model in the OPNET Modeler, and
  a DAA antenna based testbed to evaluate and validate the proposed solutions. The algorithms
  and protocols proposed in this project can be applied in the WiMAX product development and are
  expected to impact the standardization activities in IEEE and IETF. The relay station model and the
  testbed can be used by other researchers for evaluation and validation. Moreover, the proposed
  research activities will complement and enrich the curriculum on wireless communications and networking.
  
  
• Cross-layer Optimization for Dynamic Spectrum Access Wireless Mesh Networks
  Award #: 0721880
  Sponsor: NSF Networking Technology and Systems (NeTS) Program
  PI: Jian Tang
  Duration: 09/2007 - 08/2010
  Project Description: In this project, the PIs will focus on the emerging Dynamic Spectrum Access
  (DSA) Wireless Mesh Networks (WMNs). Cross-layer design is strongly needed for such a network
  due to its two special features; dynamic spectrum availability and spectrum heterogeneity. Quite
  different from other well-studied wireless networks, such as mobile ad hoc networks and wireless sensor
  networks, the major concerns of WMNs are throughput, fairness, and QoS support, instead of mobility
  support and power efficiency. The PIs plan to conduct a comprehensive study on cross-layer optimization
  in DSA WMNs, and design protocols under the guidance of this cross-layer optimization. They will
  concentrate on the bottom four layers of the network stack and seek joint congestion control, routing,
  spectrum sharing, and power control solutions with the objective of maximizing throughput, achieving
  certain fairness, and providing QoS support. Furthermore, the research will be conducted under various
  network models including different interference models, traffic models and fairness models.

• WiMAX-Based Relay Node with Smart Adaptive Antennas for Mesh Networking
  Award #: 09-23
  Sponsor: Montana Board of Research and Commercialization Technology (MBRCT)
  PIs: Jian Tang, Richard S. Wolff and Yikun Huang
  Duration: 08/2008 - 07/2010
  Project Description: The goal of this project is to develop a relay node and networking software
  infrastructure that will leverage a compact, low-cost smart adaptive antenna system (developed at MSU)
  and new radio technologies to provide robust, long-range and high-speed wireless communications.

• Ad Hoc Routing For Rural Public Safety
  Award #: 2007-ST-086-000001
  Sponsor: Safecom Program, Department of Homeland Security
  PIs: Doug Galarus, Richard Wolff, Jian Tang and Dave Larson
  Duration: 03/2007 - 08/2008
  Project Description: Providing responsive and effective public safety requires highly coordinated
  and interoperable communications infrastructure and information systems. Achievement of these goals
  is particularly challenging in rural and sparsely populated areas, where the lack of communications
  infrastructure, large distances and difficult terrain contribute additional complexities. In this project,
  The PIs explored the feasibility of using mobile ad hoc networks in rural areas for public safety related
  emergency communications. Specifically, they evaluated existing standard ad hoc routing protocols
  using the real-life rural emergency scenarios and developed a novel QoS aware protocol to meet all the
  performance requirements.
  
  

 

 

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