• Financial System
  • OA
  • Bidding
  • Email
  • 简体中文
  • About Us
    • Overview
    • History
    • Organization
    • Our Team
      • Management
      • Research & Development
      • Functional Personnel
  • Research Divisions
    • Division of Fundamental Research
    • Center for General Software and Technologies of Big Data
      • Optimization Solver Development Laboratory
    • Center for Big Data Applications and Technologies
      • Data-driven Intelligent Information System Laboratory
      • Medical Big Data Laboratory
      • Human Language Technology Laboratory
      • Laboratory for Smart City, Transportation and Logistics Big Data
      • Public and Judicial Big Data Laboratory
      • Shenzhen International Center for Industrial and Applied Mathematics (SICIAM) 
  • Our Research
    • R&D Projects
    • Educational Programs
      • CUHKSZ-SRIBD Joint PhD&Postdoc Programs
      • Scholarship Program
    • Seminars & Conferences
  • News
    • SRIBD News
    • MIIS
    • Audios & Videos
      • Videos
      • Periodicals
  • Recruiting
    • Reserach Scientist
    • Engineer
    • Administration Staff
    • Recruitment Overview
  • Contact Us
    • Inquiry
    • Public Relations
    • Cooperation
  • About Us
    • Overview
    • History
    • Organization
    • Our Team
      • Management
      • Research & Development
      • Functional Personnel
  • Research Divisions
    • Division of Fundamental Research
    • Center for General Software and Technologies of Big Data
      • Optimization Solver Development Laboratory
    • Center for Big Data Applications and Technologies
      • Data-driven Intelligent Information System Laboratory
      • Medical Big Data Laboratory
      • Human Language Technology Laboratory
      • Laboratory for Smart City, Transportation and Logistics Big Data
      • Public and Judicial Big Data Laboratory
      • Shenzhen International Center for Industrial and Applied Mathematics (SICIAM) 
  • Our Research
    • R&D Projects
    • Educational Programs
      • CUHKSZ-SRIBD Joint PhD&Postdoc Programs
      • Scholarship Program
    • Seminars & Conferences
  • News
    • SRIBD News
    • MIIS
    • Audios & Videos
      • Videos
      • Periodicals
  • Recruiting
    • Reserach Scientist
    • Engineer
    • Administration Staff
    • Recruitment Overview
  • Contact Us
    • Inquiry
    • Public Relations
    • Cooperation
  • Financial System
  • OA
  • Bidding
  • Email
  • 简体中文
  • About Us
    • Overview
    • History
    • Organization
    • Our Team
      • Management
      • Research & Development
      • Functional Personnel
  • Research Divisions
    • Division of Fundamental Research
    • Center for General Software and Technologies of Big Data
      • Optimization Solver Development Laboratory
    • Center for Big Data Applications and Technologies
      • Data-driven Intelligent Information System Laboratory
      • Medical Big Data Laboratory
      • Human Language Technology Laboratory
      • Laboratory for Smart City, Transportation and Logistics Big Data
      • Public and Judicial Big Data Laboratory
      • Shenzhen International Center for Industrial and Applied Mathematics (SICIAM) 
  • Our Research
    • R&D Projects
    • Educational Programs
      • CUHKSZ-SRIBD Joint PhD&Postdoc Programs
      • Scholarship Program
    • Seminars & Conferences
  • News
    • SRIBD News
    • MIIS
    • Audios & Videos
      • Videos
      • Periodicals
  • Recruiting
    • Reserach Scientist
    • Engineer
    • Administration Staff
    • Recruitment Overview
  • Contact Us
    • Inquiry
    • Public Relations
    • Cooperation

Breadcrumb

  • Home
  • Our Research
  • R&D Projects
  • Modeling the Mapping of Network KQI to User QoS in Wireless Systems

Modeling the Mapping of Network KQI to User QoS in Wireless Systems

Mar 27,2023 Projects

Project description/goals

To model the relation for the key perfomance indicators of base station/cell and the user Quality of Service (e.g. throughput).

Importance/impact, challenges/pain points

With an accurate and interpretable relationship model, we will be able to manage network resources more efficiently with a guranteed network performance.

Since the relationship is highly nonlinear, we need to develop it in a data-driven way by using black box model. However, two issues arise. First, real world data tend to have overwhelming noise and could be destructive to the black box model. Second, we also need to do inference on the complicated black box model for further applications.

Solution description

For the first challenge, we propose a robust training mechanism based on bi-level optimization that can automatically assign lower weight for the samples with heavier noise.

For the second challenge, we propose two techniques:

1. A bootstrap assisted SHAP value for interpreting the prediction process of black box model.

2. A bi-section method for computing the marginal threshold of target KPI that guarantees an acceptable network performance.

Key contribution/commercial implication

Our project can be applied to many scenairos, for example :

1. Engineer can employ the prediction model for tuning the parameters of base station, in order to achieve better network performance.

2. When an extreme event occurs, engineer can determine which KPI is the key reason that cause this event.

3. Engineer can quickly determine the minimal requirement of target KPI that can provide acceoptable network performance.

Next steps

In the next steps, we could develop larger and more precise prediction model by collecting massive and multi-scale (grid-level, region-level, user-level, etc.)  data. Such model can provide a firm foundation for massive automatic network parametes tuning in the future.

Besides, we can also develop model that can predict the user's expectation for the network performance. Therefore, combining with our project, we can achieve users' experience guided  network parameter tuning.

Collaborators/partners

Huawei Company

Team/contributors

Zhiwei Tang, Siliang Zeng, Wenqiang Pu

 
Follow Us
  •  
  •  
  •  
  •  
  • Contact Us
    • Inquiry
    • Public Relations
    • Cooperation
  • Collaborative Institutions
    • The Chinese University of Hong Kong, Shenzhen
    • National Health Data Institute (Shenzhen)
    • Shenzhen International Center for Industrial and Applied Mathematics

  • Friendly Links
Copyright 2022 All Rights Reserved | 粤ICP备16049670号