Intelligent Hypergraph-Driven Resource Orchestration Framework For Dynamic 5g Cloud-Ran Environments
DOI:
https://doi.org/10.63949/crinfo.v2i1.001Keywords:
- 5G Networks, Cloud Radio Access Network, Spectral Efficiency, Resource Allocation, Traffic Prediction, Fairness Optimization, Network Scalability
Abstract
The 5G wireless networks require very high data rates, low latency, and huge connectivity that is very challenging to manage effectively. Cloud Radio Access Network (C-RAN) is a wireless network that concentrates the baseband processing at a pool of Base Station called Baseband Unit and implements Remote Radio Head to improve the coordination, spectral efficiency and even save of energy. Nevertheless, user behavior and changing traffic dynamics make it difficult to allocate the distribution of resources fairly and efficiently. In this work, the authors suggest a smart 5G C-RAN resource allocation model that combines hypergraph-based demand clustering, quantum-inspired adaptive resource mapping, fractal-based load forecasting, and a multi-agent game-theoretic regulator to achieve fairness. BBU pool is a coordination of virtual base stations based on predictive and self-evolving allocation logic. The results of the simulator in realistic traffic conditions illustrate an increase in the spectral efficiency, a reduction in unmet demand and fairness in an improved way when compared to the traditional methods, which show scalability and long-term operational efficiency of the next-generation C-RAN networks.
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