A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio

In this paper, a blind multiband spectrum sensing (BMSS) method requiring no knowledge of noise power, primary signal and wireless channel is proposed based on the K-means clustering (KMC). In this approach, the KMC algorithm is used to identify the occupied subband set (OSS) and the idle subband se...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Central South University 2018-10, Vol.25 (10), p.2451-2461
Main Authors: Lei, Ke-jun, Tan, Yang-hong, Yang, Xi, Wang, Han-rui
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Cognitive radio
Computer simulation
Engineering
Information theory
Metallic Materials
Noise
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a blind multiband spectrum sensing (BMSS) method requiring no knowledge of noise power, primary signal and wireless channel is proposed based on the K-means clustering (KMC). In this approach, the KMC algorithm is used to identify the occupied subband set (OSS) and the idle subband set (ISS), and then the location and number information of the occupied channels are obtained according to the elements in the OSS. Compared with the classical BMSS methods based on the information theoretic criteria (ITC), the new method shows more excellent performance especially in the low signal-to-noise ratio (SNR) and the small sampling number scenarios, and more robust detection performance in noise uncertainty or unequal noise variance applications. Meanwhile, the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading. Simulation result verifies the effectiveness of the proposed method.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-018-3928-z