Weighted adaptive load and handoff equilibrium: a novel resource allocation framework for cloud radio access networks with load prediction and handoff optimization

The effective assignment of Remote Radio Units (RRUs) to Baseband Units (BBUs) in Cloud Radio Access Networks (C-RAN) remains a critical challenge, particularly in large-scale deployments where dynamic load patterns and frequent handoffs necessitate rapid adaptation. This thesis addresses this challenge through an innovative optimization approach that balances computational efficiency with performance in RRU-BBU assignments. The thesis presents a novel approach through the development of the WeigHted Adaptive Load and handoff Equilibrium (WHALE) framework, addressing three critical challenges: RRU load prediction, handoff optimization, and load balancing. The prediction framework combines Long Short-Term Memory (LSTM) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. In moderate-scale tests, WHALE achieves most possible handoff transitions with nearly half the reduction in execution time. For large-scale scenarios, it significantly reduces execution time while maintaining performance comparable to Genetic Algorithm (GA). The prediction-based variant (WHALE-P) achieves nearly all of GA's performance in handoff optimization and outperforms GA in load balancing with notably lower error rates. Keywords: Cloud Radio Access Networks, RRU Load Prediction, Handoff Optimization, LSTM-GARCH