"Validation Request: Dataset Generation Pipeline for MIMO Channel Realizations using Sionna

[natanzi]

Dear Sionna Community,

I'm working on implementing a dataset generation pipeline for MIMO systems using Sionna, and I'd appreciate your feedback on my approach. Here's a detailed breakdown of my implementation:

Project Overview:

Purpose: Generating synthetic datasets for training ML models in MIMO systems
Focus: Channel realizations and proper normalization for unit average power
Implementation: Using Sionna's RayleighBlockFading channel model with TensorFlow backend
Current Configuration:

MIMO Setup:

Antenna Configuration: 4x4 MIMO system
Resource Grid: 14 subcarriers × 64 OFDM symbols
Batch size: 440 samples per generation
Channel Generation Process:

Using RayleighBlockFading model
Complex64 dtype for channel representations
Channel shape: (batch_size, num_users, num_rx_ant, num_tx_ant, num_subcarriers, num_ofdm_symbols)
Implementing proper reshaping: (440, 4, 4, 14, 64) → (440, 4, 3584)
Normalization Pipeline:

Pre-scaling for very small power values (~3.98e-18 → ~3.98e-08)
Applying scaling factor (~5.01e+03) to achieve unit power
Verification of channel statistics:
Real part: Mean ≈ 0, Std ≈ 0.707
Imaginary part: Mean ≈ 0, Std ≈ 0.707
Final channel power: 1.0000 (normalized)
Current Output Validation:

Channel realizations show expected Gaussian distribution
Power normalization achieving target of 1.0 with minimal error (~1.19e-07)
Proper tensor management between TensorFlow and NumPy operations
Questions for the Community:

1. Is this approach aligned with best practices for channel generation using Sionna?
2. Are the normalization steps appropriate for ensuring consistent channel power?
3. Are there any potential improvements or optimizations you'd recommend?
4. Is the reshape operation (440, 4, 4, 14, 64) → (440, 4, 3584) appropriate for MIMO processing?

Thank you for your time and expertise.
Milad