CVaR-Constrained Optimization for Risk-Aware Dynamic Resource Allocation in 5G RAN Slicing

Dynamic resource allocation across heterogeneous network slices in 5G radio access networks requires balancing conflicting quality-of-service (QoS) requirements under time-varying and bursty traffic conditions. Conventional approaches rely on percentile-based latency metrics such as the 95th-percent...

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Autori principali:	S. Mahalakshmi, S. Markkandan
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2026-01-01
Serie:	IEEE Open Journal of the Communications Society
Soggetti:	5G RAN slicing resource allocation deep reinforcement learning proximal policy optimization (PPO) conditional value-at-risk (CVaR) constrained Markov decision process (CMDP)
Accesso online:	https://ieeexplore.ieee.org/document/11550086/
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