Agile 5G Networks: Advance Traffic Steering xAPP for Interference Mitigation

The Open Radio Access Network (O-RAN) signifies a substantial advancement in the telecommunications industry, with xAPPs and the RIC platform forming the core of its intelligence. By harnessing the agility of 5G and the capabilities of O-RAN, we can deploy intelligent applications, particularly for resilience and interference mitigation. We conduct a comprehensive analysis of the most prevalent xAPP service model in O-RAN, namely traffic steering, discovering its data flow and intelligent components. We identify challenges, propose solutions, and validate their effectiveness using O-RAN’s data and a system-level simulation. The results demonstrate significant enhancements in the accuracy and latency of the intelligence model, along with an 8.9% increase in throughput in the simulation.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/milcom61039.2024.10773839
Citations
6
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
1.25
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article

Agile 5G Networks: Advance Traffic Steering xAPP for Interference Mitigation

Connor McPeak, Daniel J. Jakubisin, Jeffrey H. Reed, Hanchao Yang et al.
6 citations
Advanced MIMO Systems Optimization
1.25
article

Agile 5G Networks: Advance Traffic Steering xAPP for Interference Mitigation

Connor McPeak, Daniel J. Jakubisin, Jeffrey H. Reed, Hanchao Yang, Nishith Tripathi, Gustave Anderson, Yaling Yang, Rakesh Nagampally
article en
6 citations

Abstract

The Open Radio Access Network (O-RAN) signifies a substantial advancement in the telecommunications industry, with xAPPs and the RIC platform forming the core of its intelligence. By harnessing the agility of 5G and the capabilities of O-RAN, we can deploy intelligent applications, particularly for resilience and interference mitigation. We conduct a comprehensive analysis of the most prevalent xAPP service model in O-RAN, namely traffic steering, discovering its data flow and intelligent components. We identify challenges, propose solutions, and validate their effectiveness using O-RAN’s data and a system-level simulation. The results demonstrate significant enhancements in the accuracy and latency of the intelligence model, along with an 8.9% increase in throughput in the simulation.

Lockheed Martin (United States) (US), Virginia Tech (US)
Climate action
Openalex Percentile: Top 19%
Advanced MIMO Systems Optimization
1.25
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