A Neural Network Detector for Spectrum Sensing under Uncertainties
Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form expression. As a result, spectrum sensing under parameter uncertainty remains an open question, though many detectors exploiting specific features of a primary signal have been proposed and have achieved reasonably good performance. In this paper, a neural network is trained as a detector for modulated signals. The result shows by training on an appropriate dataset, the neural network gains robustness under uncerta
Resource information
- Authors
- Ye, Ziyu, Peng, Qihang, Levick, Kelly, Rong, Hui, Gilman, Andrew, Cosman, Pamela, Milstein, Larry
- Research area
- Artificial intelligence
- Research topics
- Signal Processing (eess.SP), Information Theory (cs.IT), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences
- Publication date
- 2019
- DOI
- 10.48550/arxiv.1907.07326
- Upstream last modified
- Feb 27, 2022
- License
- arXiv.org perpetual, non-exclusive license
- Last record update
- Nov 7, 2026
About this resource
Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form expression. As a result, spectrum sensing under parameter uncertainty remains an open question, though many detectors exploiting specific features of a primary signal have been proposed and have achieved reasonably good performance. In this paper, a neural network is trained as a detector for modulated signals. The result shows by training on an appropriate dataset, the neural network gains robustness under uncertainties in system parameters including the carrier frequency offset, carrier phase offset, and symbol time offset. The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.