Next Article in Journal
A Novel Parameter Estimation Method Based on a Tuneable Sigmoid in Alpha-Stable Distribution Noise Environments
Next Article in Special Issue
NovaGenesis Applied to Information-Centric, Service-Defined, Trustable IoT/WSAN Control Plane and Spectrum Management
Previous Article in Journal
New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)
Previous Article in Special Issue
Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(9), 3011; https://doi.org/10.3390/s18093011

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

1
College of Information Science & Technology, Hainan University, Haikou 570208, China
2
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570208, China
*
Author to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 27 August 2018 / Accepted: 2 September 2018 / Published: 8 September 2018
Full-Text   |   PDF [6714 KB, uploaded 8 September 2018]   |  

Abstract

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity. View Full-Text
Keywords: cognitive radio network; singular value decomposition; cooperative wideband spectrum sensing; transmission loss; modulated wideband converter; progressive support selection cognitive radio network; singular value decomposition; cooperative wideband spectrum sensing; transmission loss; modulated wideband converter; progressive support selection
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hu, Z.; Bai, Y.; Huang, M.; Xie, M.; Zhao, Y. A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing. Sensors 2018, 18, 3011.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top