Lilly Tech Systems
AI-Driven Resource Management
Explore how machine learning optimizes radio spectrum, compute, and network resources in real time to maximize 5G network capacity and efficiency.
Radio Resource Management
| RRM Function | AI Technique | Performance Gain |
|---|---|---|
| Beam Management | Deep learning for beam prediction | Reduced beam search overhead by 70% |
| Power Control | RL-based transmit power optimization | 15-25% energy savings |
| Scheduling | DRL for dynamic user scheduling | 20-40% throughput improvement |
| Handover | Predictive handover using mobility models | 50% reduction in handover failures |
Resource Optimization Pipeline
Traffic Prediction
LSTM and transformer models forecast traffic demand per cell, per slice, and per time period, enabling proactive resource allocation.
Resource Allocation
Deep reinforcement learning agents determine optimal allocation of PRBs, power, and antenna resources to maximize network utility.
Interference Management
ML models coordinate inter-cell interference by learning optimal power and beam patterns that minimize co-channel interference.
Energy Optimization
AI identifies opportunities to deactivate cells or reduce power during low-demand periods, achieving significant energy savings without service degradation.
Compute Resource Management
VNF Scaling
AI predicts when virtual network functions need scaling and triggers autoscaling before performance degradation occurs.
Workload Placement
ML optimizes placement of network functions across cloud and edge infrastructure based on latency, cost, and resource availability.
Container Orchestration
AI-enhanced Kubernetes scheduling for 5G CNFs (Cloud-Native Functions) that considers network topology and latency requirements.
GPU Resource Sharing
ML workloads at the edge share GPU resources efficiently, with AI scheduling that maximizes utilization while meeting inference deadlines.