Hybrid frequency-domain CFAR detectors for cognitive radio interference resilience and the dual-use security paradox

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Cognitive radio networks (CRNs) enable secondary users (SUs) to opportunistically access underutilized licensed spectrum while protecting primary users (PUs). Robust spectrum sensing under heterogeneous interference and heavy jamming remains challenging: conventional approaches either require extensive prior knowledge or degrade significantly under interference, while basic constant false-alarm rate (CFAR) variants remain vulnerable to structured jamming that contaminates only part of the reference window, causing fixed-rule estimators to either include contaminated samples or discard clean ones. Hybrid CFAR architectures address this by first analyzing reference-window contamination patterns and then adaptively selecting or combining estimation strategies. We adapt eleven such hybrid architectures-originally developed in the radar literature-to frequency-domain spectrum sensing in contested CRNs, and evaluate them across four families (adaptive algorithm selection, order-statistic fusion, intelligent censoring, and weighted processing) against classical CR-CFAR baselines via Monte Carlo simulations using APCO Project 25 as the PU waveform and an OFDMA-based SU, under barrage and swept-FM jamming. Results show that First-Order Difference CFAR achieves the best detection performance through global sorting and statistical jump detection ([Formula: see text] at SNR [Formula: see text] dB under 50% sweep coverage), followed by Smallest-Of and Order-Statistic Smallest-Of variants. However, this resilience creates a dual-use security paradox: unauthorized devices employing these techniques can resist administrative spectrum control. We address this through an algorithm-aware comb-sweep countermeasure that exploits CFAR reference-window dependencies, inducing near-unity false alarms on vacant channels within a 4 dB JSR denial plateau ([Formula: see text] to [Formula: see text] dB) while preserving PU detection, demonstrating that algorithm-aware enforcement can restore spectrum governance in contested environments.

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