The Big-Data-Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions, and Future Trends

The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, the authors introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. The authors also identify the required data and respective workflows that facilitate intelligent network optimizations. It is the authors' strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of the authors' proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.

• Record URL:
  https://doi.org/10.1109/MVT.2017.2752758
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15566072
• Supplemental Notes:
  • Copyright © 2017, IEEE.
• Authors:
  • I, Chih-Lin
  • Sun, Qi
  • Liu, Zhigang
  • Zhang, Siming
  • Han, Shuangfeng
• Publication Date: 2017-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 20-29
• Serial:
  • IEEE Vehicular Technology Magazine
  • Volume: 12
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1556-6072
  • Serial URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=34840

Subject/Index Terms

• TRT Terms: Artificial intelligence; Machine learning; Optimization; Wireless communication systems
• Uncontrolled Terms: Big data; Network architecture
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01655642
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 2 2018 10:38AM