Publikation
ML-Driven Service-Aware Radio Resource Allocation for Open RAN–Enabled 6G Networks
Devanshu Anand; Michael Gundall; Hans Dieter Schotten
In: 2026 IEEE 37th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC-2026), IEEE, 9/2026.
Zusammenfassung
Sixth-generation (6G) mobile networks are expected
to support highly heterogeneous services with stringent and
diverse quality-of-service (QoS) requirements, exposing the limi-
tations of conventional throughput-centric radio access network
(RAN) control mechanisms. To address these challenges, AI-native
RAN architectures integrating machine learning (ML) into control
loops have gained significant attention. In this paper, we propose
SAIL-RAN (Service-Aware Intelligent Learning for Open RAN),
an ML-driven and uncertainty-aware MAC-layer radio resource
allocation framework for Open RAN–enabled 6G networks. SAIL-
RAN employs ML-based service urgency prediction to proactively
identify users at risk of service-level agreement (SLA) violations
and dynamically adapt resource allocation via the near-real-
time RAN Intelligent Controller (RIC), enabling risk-aware and
QoS-driven MAC-layer scheduling. The proposed framework is
implemented and evaluated using a realistic end-to-end simulation
testbed integrating ns-3, ns-O-RAN, and a THz channel model.
Simulation results under heterogeneous traffic conditions show
that SAIL-RAN significantly reduces SLA violation probability,
tail latency, and jitter compared to conventional baseline schemes,
while maintaining comparable throughput and spectral efficiency.
These results demonstrate the effectiveness of uncertainty-aware,
learning-driven RAN control in meeting the service-centric re-
quirements of future 6G networks.