Telecommunicationssequential decision-making under feedbackresearch/proposed

RL-based radio access network parameter optimization training for 5G/B5G

Use reinforcement learning to teach network-control software how to adjust radio network settings by trial and feedback so the mobile network performs better.

10.0

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Executive Brief

Business Problem Solved

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Value Drivers

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Strategic Moat

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Technical Analysis

Model Strategy

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Data Strategy

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Implementation Complexity

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Scalability Bottleneck

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Market Signal

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Differentiation Factor

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Key Competitors

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Executive brief, technical architecture, and market positioning for this use case.

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Part of Application

Adaptive RAN Resource Optimization

2 use cases in this application

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Source

https://ieeexplore.ieee.org/document/9844032/