In fast-moving RF ecosystems—where latency budgets are tight, edge devices must stay lean, and reproducibility is king—RF data processing can’t afford to wait on CPU bottlenecks. The work presented here shows how to GPU-accelerate the entire RF feature extraction + grid reconstruction pipeline with CUDA, while keeping a clean CPU fallback for reviewers, auditors, and engineers without GPUs.
The Problem
Modern RF sensing pipelines need to:
- Extract per-band features from SDR IQ data.
- Smooth trajectories with filtering.
- Reconstruct dense 3D RF grids from sparse samples.
On CPU alone, these steps become a bottleneck, especially at higher resolutions. For Shenzhen-style deployments—real-time SDRs, IoT mesh systems, 5G/6G testbeds—the pipeline must run fast, reproducible, and portable.
The Solution: CUDARFDataProcessor
The paper introduces CUDARFDataProcessor, a CUDA/CuPy-accelerated class with CPU fallbacks. Core modules include:
process_iq_data– GPU FFT + per-band features (mean, max, std, sum).apply_kalman_filter– GPU Kalman smoother for noisy paths, weighted by signal strength.create_rf_grid– Dense 3D grid interpolation with inverse distance weighting on GPU.
If CUDA isn’t available, NumPy substitutes seamlessly. This design ensures results are reviewer-safe and reproducible.
Headline Results
TABLE I – Performance Snapshot
| Metric | Value | Notes |
|---|---|---|
| Feature time | 3.17 ms | CPU fallback |
| Grid time | 207 ms | Grid = 32×32×32 |
| Kalman RMSE | 0.02 | Synthetic trajectory |
TABLE II – Ablation: Window Length vs Grid Size
| Samples | Grid Points | Feature (ms) | Grid (ms) |
|---|---|---|---|
| 20k | 32,768 | 2.15 | 231.65 |
| 20k | 110,592 | 2.15 | 809.16 |
| 80k | 32,768 | 1.54 | 204.78 |
| 160k | 262,144 | 3.17 | 1752.23 |
Takeaway: Grid reconstruction dominates runtime, but CUDA keeps scaling manageable, even at high resolution. Feature extraction stays near-constant.
Figures at a Glance
- Fig. 1: Throughput vs. IQ window length (dashed = CPU fallback).
- Fig. 2: Speedup vs. grid resolution for
create_rf_grid.
Both plots are auto-generated at compile time. No manual figure editing: reproducibility is engineered in.
Why It Matters
- Real-Time SDR: Millisecond-scale feature extraction opens the door for beamforming controllers and adaptive interference mitigation.
- Dense RF Maps: Voxelized grids support NeRF-style visualization, coverage planning, and RF anomaly detection.
- Reproducibility: Auto-generated LaTeX tables + figures mean results are transparent, portable, and auditable—a must for cross-lab collaboration.
This is exactly the kind of kit Guangdong’s RF labs demand:
- Compact class, one-command reproducibility.
- CPU fallback for audit-friendly builds.
- GPU acceleration where it counts (grids).
- Auto-benchmark harness for clean comparisons.
⚙️ Mantra: Engineer small, accelerate critical paths, ship reproducible pipelines.
Closing Note
This CUDA-accelerated RF processor doesn’t just shave milliseconds—it reframes how RF pipelines are benchmarked and shared. With built-in reproducibility, the results are not only faster but trustworthy.