Cognitive Radio Applications of Rapid RF Triage: FFT-Only Wins at the Edge

How a 1.5 ms FFT + Hybrid Gate Enables Real-Time Dynamic Spectrum Access

By Benjamin J. Gilbert
Spectrcyde RF Quantum SCYTHE
bgilbert2@com.edu
Full Paper PDF · Reproducible Code

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Cognitive Radio in 2025: The Core Loop

graph TD
    A[RF Environment] --> B[Sense Spectrum]
    B --> C{Triage: Digital or Analog?}
    C -->|Analog (FM, AM)| D[Vacate Band]
    C -->|Digital (BPSK, QPSK...)| E[Share / Opportunistic TX]
    E --> F[Transmit]
    F --> A

Triage must be <10 ms, <1M FLOPs, >0.7 AUROC at 0 dB SNR.

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Application 1: CBRS-Style Dynamic Spectrum Access (DSA)

Band	Primary User	Secondary User
3.55–3.7 GHz	DoD Radar	LTE/5G-NR (CBRS)

Triage Role:

• Detect radar (analog) → evacuate in <60 s (FCC rule)
• Detect LTE/5G (digital) → coexist

Why FFT Wins:

Metric	FFT+Filter	Tiny CNN
p99 Latency @ 0 dB	1.5 ms	6.0 ms
Compute	0.25M FLOPs	12M
AUROC	0.754	0.671

FFT meets 60 s evacuation with 40× margin.

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Application 2: 6G Unlicensed Shared Spectrum

• Sub-7 GHz + mmWave
• AI-Native Air Interface
• Zero-touch spectrum arbitration

Hybrid Gate in Action:

def triage(iq):
    fft_mag = normalize(fft(iq, n=1024))
    conf, label = light_filters(fft_mag)  # 1.5 ms

    if conf > 0.9:
        return label                       # Fast path
    else:
        return cnn(fft_mag)               # Slow path (rare)

Gate Rate	Avg Compute	p99 Latency	Use Case
5%	0.8M FLOPs	1.5 ms	6G Edge Node
20%	2.6M FLOPs	1.5 ms	Dense Urban

5% gate rate = 15× savings vs pure CNN, same tail latency.

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Application 3: Anti-Jamming in Contested Environments

Jammer Type	Spectral Signature
FM Sweep	Smooth, wideband
BPSK Pulse	Sharp tones

Triage → Countermeasure:

Detected	Action
Analog (FM)	Frequency hop away
Digital (BPSK)	Null the beam

FFT peak spacing detects symbol rate in <2 ms.

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Application 4: Federated Spectrum Sensing Networks

• 1000s of IoT sensors
• Each runs FFT triage locally
• Only ambiguous samples → cloud CNN

graph LR
    S1[Sensor 1] -->|FFT: 0.95 conf| Local Decision
    S2[Sensor 2] -->|FFT: 0.70 conf| Cloud CNN
    Cloud --> Fusion[Global Map]

>95% decisions stay on-device → near-zero backhaul.

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Real-World Integration: USRP + GNU Radio Block

// gr-fft_triage.cc
class fft_triage : public gr::sync_block {
    float compute_confidence(float* mag) {
        float energy = band_energy(mag);
        float spacing = peak_spacing(mag);
        return sigmoid(energy * spacing);  // <1 ms
    }
};

• Input: 1024 IQ samples
• Output: 0 (analog) or 1 (digital) + confidence
• Latency: 1.5 ms on E312

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Performance in Cognitive Radio Scenarios

Scenario	Required p99	FFT+Filter	Hybrid (f=0.1)	Pure CNN
CBRS SAS	<50 ms	Pass	Pass	Pass
6G Zero-Touch	<5 ms	Pass	Pass	Fail
EW Triage	<2 ms	Pass	Pass	Fail

Only FFT-based methods meet sub-5 ms budgets.

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Open Challenge to the Community

Can you beat 0.754 AUROC with <1M FLOPs at 0 dB SNR?

git clone https://github.com/bgilbert1984/rf-triage-benchmark
make challenge

Submit your model → leaderboard.

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TL;DR: Cognitive Radio Stack in 2025

Layer	Technique	Latency	Compute
L0	Energy Detection	0.1 ms	10k FLOPs
L1	FFT + Light Filters	1.5 ms	0.25M FLOPs
L2	Hybrid Gate → Tiny CNN	1.5 ms (p99)	0.8M FLOPs (f=0.1)
L3	Full DL	Offline	Cloud

FFT is the new cognitive radio baseline.

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Read the paper: FFT-Only vs Learned Spectral Proxies (PDF)
Run the code: github.com/bgilbert1984/rf-triage-benchmark
Cite:

@article{gilbert2025fft,
  title={FFT-Only vs Learned Spectral Proxies for Rapid RF Triage},
  author={Gilbert, Benjamin J.},
  journal={arXiv preprint},
  year={2025}
}

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Published October 29, 2025
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