End-to-end deep learning-based compressive spectrum sensing in cognitive radio networks

Xiangyue Meng, Hazer Inaltekin, Brian Krongold

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

11 Citations (Scopus)


In cognitive radio networks, compressive sensing has the potential to allow a secondary user to efficiently monitor a wideband spectrum at a sub-Nyquist sampling rate without complex hardware. In general, compressive sensing techniques leverage the assumption of sparsity of the wideband spectrum to recover the spectrum by solving a set of ill-posed linear equations. In this paper, we adopt the framework of a generative adversarial neural network (GAN) in deep learning and propose a deep compressive spectrum sensing GAN (DCSS-GAN), where two neural networks are trained to compete with each other to recover the spectrum from undersampled samples in the time domain. The proposed DCSS-GAN is a data-driven learning approach that does not require a priori statistics about the radio environment. In addition, it is an end-to-end algorithm that directly recovers the information of spectrum occupancy from raw samples and without the need of energy detection. Various simulations show that the proposed DCSS-GAN has a 12.3 to 16.2 performance gain on prediction accuracy at a 1/8th compression ratio compared to the conventional LASSO approach.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781728150895
ISBN (Print)9781728150901
Publication statusPublished - 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020

Publication series

ISSN (Print)1550-3607
ISSN (Electronic)1938-1883


Conference2020 IEEE International Conference on Communications, ICC 2020


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