Pyomnet-AI: Enabling Efficient OMNeT++ Network Optimization Through Low-Overhead, Hybrid Python AI Integration
Roshdi M, Chakraborty A, Amri A, Raghunandan S, German R (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 163-170
Conference Proceedings Title: 2026 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2026
Event location: Malaga
ISBN: 9798331570194
DOI: 10.1109/EuCNC/6GSummit68295.2026.11577580
Abstract
Integrating AI/ML workflows into OMNeT++ is increasingly important for adaptive network optimization, especially in 5G-Advanced and emerging 6G RAN studies, where learned policies can improve scheduling, mobility management, and resource utilization. However, pure C++ integrations are often insufficient because modern AI development is largely centered around Python ecosystems and toolchains. Additionally, existing Python-based AI integration approaches mostly rely on socket communication, introducing serialization and coordination overhead which is not well suited to latency sensitive or high call frequency integration scenarios. This paper therefore presents PyOMNeT-AI, a framework for AI integration in OMNeT++ built around three complementary patterns. PyCoSim enables bidirectional co-simulation with Python modules as first-class simulation components. PyEmbed provides scoped interpreter embedding for inference-centric workloads, achieving a baseline overhead of 0.28 μs and sustaining over 3 million calls per second. PyShm provides shared-memory coupling for Gymnasiumcompatible RL training, yielding a 2-6 × speedup over socketbased alternatives depending on workload and payload size. We present the design rationale, performance characterization, and pattern-selection guidelines, validated through Simu5Gbased RAN studies and designed to transfer to other OMNeT++ application domains.
Authors with CRIS profile
Moustafa Mahmoud Ahmed Mohamed Roshdi
Reinhard German Computer Science 7 (Computer Networks and Communication Systems)
Reinhard German Computer Science 7 (Computer Networks and Communication Systems)
Involved external institutions
Germany (DE)How to cite
APA:
Roshdi, M., Chakraborty, A., Amri, A., Raghunandan, S., & German, R. (2026). Pyomnet-AI: Enabling Efficient OMNeT++ Network Optimization Through Low-Overhead, Hybrid Python AI Integration. In 2026 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2026 (pp. 163-170). Malaga, ES: Institute of Electrical and Electronics Engineers Inc..
MLA:
Roshdi, Moustafa, et al. "Pyomnet-AI: Enabling Efficient OMNeT++ Network Optimization Through Low-Overhead, Hybrid Python AI Integration." Proceedings of the 2026 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2026, Malaga Institute of Electrical and Electronics Engineers Inc., 2026. 163-170.
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