Deep Learning for Network Slicing and Self-Healing in 5G Systems

Haque, Mohammed Mobasserul; Agrawal, D.C.; Dixit, Pranat; Bhattacharyya, Budhaditya

doi:10.1109/spicscon54707.2021.9885574

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Core architecture evolutions and APIs need rich feature observability and elastic reconfigurability to the interfaces for enabling on-demand, autonomous, predictability, and dynamic configurations. Therefore, the convergence of well-known enabling NS paradigms with prediction-based deep learning and autonomy-based deep reinforcement learning (DRL) introduces innovative schemes (e.g., resource sharing, elastic virtualization, and QoS-driven isolation between slices), which can be evaluated by the reward functions of QoS-aware agents [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

QoS-Driven Slicing Management for Vehicular Communications

Tam,

Ros,

Song

et al. 2024

Electronics

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Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

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Section: Introductionmentioning

confidence: 99%