Tutorial: AI-Assisted Spectrum Classification

This tutorial gives a practical, step by step approach to building AI-assisted spectrum classification systems you can run in a lab or prototype on edge hardware. I focus on real inputs, reproducible datasets, and techniques that are robust across signal to noise ratios. The goal is not to survey every paper but to provide a compact workflow you can implement with common SDRs, public datasets, and current model families.

1) Define the problem and the scope Decide what you want to classify. Common tasks are automatic modulation classification, emitter type identification, and signal presence or bandwidth identification. Each task has different performance targets and data demands. For example, public RadioML-style datasets label modulation and provide multi-SNR slices that are ideal for benchmarking modulation classifiers.

2) Hardware and data collection If you are collecting your own data use an SDR suited to the bands and dynamic range you need. Low-cost RTL-SDR dongles are fine for learning and narrowband UHF work but are limited in sample rate and ADC resolution. For production-quality collection use higher performance radios such as USRPs or SDRPlay variants with stable clocks and wider front ends. Document antenna, preselector, gain settings, sampling rate, and time stamps for every capture. GNU Radio and vendor SDKs provide stable capture pipelines you can automate.

3) Preprocessing pipeline Work on raw complex I/Q when possible. Typical preprocessing steps are DC offset removal, coarse frequency offset correction, normalization, decimation or resampling, windowing, and creating fixed length frames. Keep a copy of raw I/Q so you can reprocess with different transforms. GNU Radio flowgraphs or simple Python scripts with numpy/scipy handle these steps quickly.

4) Feature representations: raw I/Q, spectrograms, and hybrids There are three practical representation families:

Raw I/Q time series. Models operating directly on complex samples can exploit phase structure and cyclostationarity. Complex-valued convolutions and architectures tuned to complex input often give top performance.
Spectrograms or time frequency images. Converting blocks of I/Q into short time Fourier transform spectrograms turns the problem into an image classification task. This often works well when frequency content is the primary discriminant and it enables reuse of mature CNN and vision toolchains. Recent work shows that properly tuned spectrogram resolution and multi-scale transforms perform competitively.
Hybrid representations. Combining I/Q and spectral channels or stacking multiple transforms into a multi-channel input frequently improves robustness, especially across varying SNRs.

5) Model families and architectural choices Start simple and iterate upward.

CNNs. Standard convolutional networks trained on spectrograms or real/imaginary channels remain a reliable baseline for many tasks. There are many open implementations you can borrow.
Complex CNNs. If you keep complex I/Q, try complex-valued convolutional layers. These layers are mathematically better matched to RF signals and often yield measurable accuracy gains.
Transformers and attention-based models. Recent work has shown transformer encoders adapted to time series can outperform CNNs at low SNRs by better capturing global temporal context. They work well when you have moderate to large datasets and sufficient compute.
Ensembles and Mixture of Experts. Combining specialist models for low and high SNR regimes via gating or MoE can yield robust across-the-board performance.

6) Data augmentation and domain robustness Radio signals are sensitive to hardware, propagation, and channel effects. Use domain-specific augmentations such as random time shifts, frequency offsets, fading simulations, additive interference, and SNR mixing. Recent augmentation toolkits and papers report large gains in few shot and imbalanced regimes. Transfer learning and domain adaptation strategies are useful when you train on synthetic or lab datasets but deploy over-the-air.

7) Training recipe and evaluation

Train with SNR-labeled data and track accuracy versus SNR curves. That curve is the single most informative evaluation for RF classifiers. Public RadioML datasets include labeled SNR sweeps you can use as a baseline.
Use confusion matrices and per-class recall to find ambiguous pairs. Many modulations are easily confused at low SNR. Calibrate classifier thresholds if you need high precision on a subset of classes.
Hold out real-world captures for final validation to catch dataset shift. Synthetic datasets are useful for development but do not fully represent over-the-air impairments.

8) Lightweight deployment For edge devices quantize models, prune unneeded parameters, or use knowledge distillation to transfer accuracy into smaller networks. Evaluate inference latency on your target hardware and measure memory and power at the same time as accuracy. Many academic results use large models that are not feasible for small radios without a careful compression step.

9) Robustness, adversarial concerns, and safety AI models for RF can be brittle to slightly perturbed inputs and to intentional adversaries. Design detection thresholds, monitor confidence, and when possible incorporate anomaly detection layers upstream of the classifier. Always respect laws and regulations. Do not perform active transmission or jamming experiments except under appropriate authorization.

10) Minimal, reproducible pipeline (example)

Data: Start with RADIOML or your own I/Q captures in 1 second blocks.
Preprocess: Remove DC, resample to a stable rate, slice into 1024 or 2048 sample frames.
Features: Compute STFT spectrogram with 256 sample window and 50 percent overlap, convert magnitude to dB and normalize per-frame.
Model: Small ResNet on the spectrogram image or a compact transformer encoder on raw I/Q. Train with Adam, initial LR 1e-3, schedule with patience-based decay. Add augmentations: random freq shift, AWGN mixing, time stretching.
Evaluate: Plot accuracy vs SNR, confusion matrices, and per-class precision. Validate with an over-the-air holdout.