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Computationally Efficient Signal Detection With Unknown Bandwidths
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DOI：10.1109/OJCOMS.2026.3656868.png)
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Abstract
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Signal detection in environments with unknown signal bandwidth and time intervals is a fundamental problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements, where the signal is constrained to an interval in one dimension or a hyper-cube in multiple dimensions. A Generalized Likelihood Ratio Test (GLRT) is derived, resulting in a straightforward metric involving normalized average signal energy for each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on signal-to-noise ratios (SNRs) and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from $O(N^{2})$ to $O(N)$ for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.
Keywords:
Spectrum sensing
efficient spectrum detection
cognitive radio
maximum likelihood estimation (MLE)
binary hypothesis testing
neural networks for signal detection
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6.1
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493
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