Adaptive Data Driven Network Slicing and Resource Blocks Assignment Using Deep Reinforcement Learning

doi:10.1007/978-3-031-94895-4_40

Adaptive Data Driven Network Slicing and Resource Blocks Assignment Using Deep Reinforcement Learning

2026-01-01

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DOI：10.1007/978-3-031-94895-4_40

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Network management complexity poses a significant challenge for dynamic prospicient grid environments. Methodologies integrating advanced machine learning techniques offer an opportunity to address these challenges. Among these, deep reinforcement learning (DRL) promises abilities to proactively monitor network resources while simultaneously analyzing device behavior, traffic patterns, and network capabilities. Coupled with the key abilities of DDDAS paradigm for creating an infosymbiotic feedback loop for data and measurement steering, these methodologies could break grounds for dynamic resource management. In this preliminary research work, we propose an adaptive data driven network slicing framework for prospicient grids using deep reinforcement learning. Once trained, the DRL dynamically adjusts network slice capacities and assignment using real-time data to foster continuous improvement, proactively anticipating future requirement changes to scheduling. Proposed DDDAS-based approach offers adaptability in resource management for evolving smart grid demands.

Keywords:

Dynamic Data Driven Applications Systems (DDDAS)

Deep Reinforcement Learning

Infosymbiotic Systems

Smart Grid

Power Gris

5G and Beyond 5G

Adaptive Data Driven Network Slicing and Resource Blocks Assignment Using Deep Reinforcement Learning

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