A Neural Network Detector for Spectrum Sensing under Uncertainties

Autor:	Ye, Ziyu, Peng, Qihang, Levick, Kelly, Rong, Hui, Gilman, Andrew, Cosman, Pamela, Milstein, Larry
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Information Theory Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 4 figures, submitted to ICNC2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.07326 Zobrazit plný text záznamu View this record from Arxiv