Description
The fifth generation (5G) of cellular communication is migrating towards higher frequenciesto cater to the demand for higher data rate applications. However, in higher frequency ranges, like mmWave and terahertz, physical blockage poses a significant challenge to the large-scale deployment of this

The fifth generation (5G) of cellular communication is migrating towards higher frequenciesto cater to the demand for higher data rate applications. However, in higher frequency ranges, like mmWave and terahertz, physical blockage poses a significant challenge to the large-scale deployment of this new technology. Reconfigurable Intelligent Surfaces (RISs) have shown promising potential in extending the signal coverage and overcoming signal blockages in wireless communications. However, RIS integration in networks requires high coordination between network notes, resulting in barriers to the wide adoption of RISs and similar IoT devices. To this end, this work introduces a practical study of integrating a remotely controlled RIS in an Open RAN (ORAN) compliant 5G private network with minimal software stack modifications. This thesis proposes using cloud technologies and ORAN features, such as the Radio Intelligent Controller (RIC) and eXternal Applications (xApps), to coordinate the RIS transparently with a 5G base station operation. The proposed framework has been integrated into a proof-of-concept hardware prototype with a 5.8 GHz RIS. Experimental results demonstrate that the framework can control the beam steering in the RIS accurately within the network. The proposed framework shows promising potential for near real-time RIS beamforming control with minimal power consumption overhead.
Reuse Permissions
  • 9.78 MB application/pdf

    Download restricted until 2026-05-01.

    Details

    Title
    • Transparent Integration of IoT devices in a 5G ORAN Network
    Contributors
    Date Created
    2024
    Subjects
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2024
    • Field of study: Computer Science

    Machine-readable links