akaplan/ris-agent-6g

Draft Agent for RIS Optimization in 6G Networks

Model Description

This is a lightweight neural network model designed for real-time Reconfigurable Intelligent Surface (RIS) phase configuration in 6G networks. The model was trained using RIS channel simulation data to predict optimal phase shifts for wireless communication optimization.

Model Details

• —Architecture: 2-layer MLP with batch normalization
• —Input: CSI (2048 dims) + semantic features (40 dims) = 2088 total dimensions
• —Output:
• —RIS phase shifts (256 elements, range [0, 2π])
• —Confidence score ([0, 1])
• —Antenna weights (64 dims for multi-user beamforming)
• —Parameters: ~700K
• —Latency: 0.2 ms per inference (CPU)
• —Framework: PyTorch

Training Data

• —Total Samples: 5000
• —Train/Val/Test Split: 70% / 15% / 15%
• —Data Source: RIS channel simulation with synthetic CSI
• —Features: Channel state information + geometric parameters
• —Labels: Optimal RIS phase configurations for SNR maximization

Training Configuration

• —Optimizer: Adam (lr=1e-3, weight_decay=1e-5)
• —Scheduler: Cosine Annealing (T_max=50)
• —Batch Size: 128
• —Epochs: 12 (early stopping)
• —Loss Function: MSE (phase prediction) + weighted BCE (confidence) + L1 regularization

Performance

• —Best Validation Loss: 0.3338
• —Final Training Loss: 0.3973
• —Inference Latency: 0.20 ms (CPU)

Intended Use

This model is designed for:

1. 1.Real-time RIS control in 6G mmWave systems
2. 2.Draft agent in speculative execution pipelines
3. 3.Low-latency decision making for URLLC applications
4. 4.Lightweight deployment on edge devices

Limitations

• —Designed for specific 6G scenario (28 GHz mmWave, 256-element RIS, 4 users)
• —Trained on synthetic but realistic CSI data
• —May require fine-tuning for different deployment scenarios
• —Requires proper channel normalization before inference

Usage

import torch
from draft_agent import DraftAgent

# Load model
model = DraftAgent(num_ris_elements=256, num_users=4)
checkpoint = torch.load('pytorch_model.bin')
model.load_state_dict(checkpoint)
model.eval()

# Inference
with torch.no_grad():
    csi = torch.randn(batch_size, 2048)  # Channel state information
    semantic_features = torch.randn(batch_size, 40)  # Context/angles/distances
    phases, confidence, weights = model(csi, semantic_features)

# Schedule RIS phases
ris_phases = (phases + 1.0) * 3.14159  # Scale to [0, 2π]

Training Details

• —Training Framework: PyTorch
• —Training Device: CPU
• —Training Time: ~6 seconds
• —Best Epoch: 1
• —Early Stopping: Triggered at epoch 12 with patience=10

Citation

If you use this model, please cite:

@software{ris_agent_draft_6g,
  title={Draft Agent for RIS Optimization in 6G Networks},
  author={Ahmet Kaplan},
  year={2026},
  howpublished={\url{https://huggingface.co/models}},
  note={PyTorch Model - Real-time RIS Configuration}
}

Model Architecture Details

Feature Extractor

• —Linear: 2088 → 256 (ReLU + BatchNorm + Dropout)
• —Linear: 256 → 256 (ReLU + BatchNorm + Dropout)

Output Heads

• —Phase Head: 256 → 128 (ReLU) → 256 (Tanh)
• —Confidence Head: 256 → 64 (ReLU) → 1 (Sigmoid)
• —Weights Head: 256 → 128 (ReLU) → 64

Contact & Support

For issues, questions, or contributions, please visit the project repository.

License

Apache License 2.0

Model trained as part of the LAM (Language Agent Model) framework for 6G RIS optimization