Jian Tang

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Research Projects

  • Big Data Enabled Wireless Networking: A Deep Learning Approach (Project Website)
    Award #:
    Sponsor: NSF CNS Core Program (Medium)
    PI: Jian Tang
    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 #:
    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 #:
    Sponsor: NSF EARS Program
    PI: Jian Tang
    Co-PI: M. Cenk Gursoy
    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
    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 #:
    Sponsor: NSF Networking Technology and Systems (NeTS) Program
    PI: Jian Tang
    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 #:
    Sponsor: Montana Board of Research and Commercialization Technology (MBRCT)
    PIs: Jian Tang, Richard S. Wolff and Yikun Huang
    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 #:
    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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