Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks
By Jun Ma, Guodong Zhao, Geoffrey Y. Li. IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference, 2008
963 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/799cce93183532ca237719bb7a0aa3686eb8e4df
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Ying-Chang Liang
- Rui Zhang
- Geoffrey Y. Li
- Yonghong Zeng
- Beibei Wang
- K. Liu
- Ian F. Akyildiz
- Brandon F. Lo
In Electrical and Electronic Engineering
- Ying-Chang Liang
- Rui Zhang
- Geoffrey Y. Li
- Yonghong Zeng
- K. Liu
- Ekram Hossain
- Ian F. Akyildiz
- Beibei Wang
Citations in the corpus
- Advances in cognitive radio networks: A survey (2011, background): “Soft decision combination for cooperative sensing based on energy detection is investigated in [77] , and maximal ratio combination is proved to be near optimal in low SNR region and reduce the SNR wall.”
- Signal Processing in Cognitive Radio (2009, methodology): “In contrast, the equal-gain combination (EGC) of the observed energies does not need any channel information and provides a reasonable performance as well [ 38 ].”
- What is a Spectrum Hole and What Does it Take to Recognize One? (2009, background): “Several groups have proposed cooperation among cognitive radios as a tool to improve performance [13], [63]–[69].”
- Ten years of research in spectrum sensing and sharing in cognitive radio (2012, background): “A soften-hard combination with two-bit overhead [46] has been proposed to provide comparable performance with less complexity and overhead.”
- Cooperative spectrum sensing in cognitive radio networks: A survey (2011, background): “Since such a soft combining scheme results in large overhead, a softened two-bit hard combining scheme is also proposed in [61] for energy detection.”
- Cognitive radio networking and communications: an overview (2011, methodology): “Ideal reporting is assumed in [44], whereas reporting channel noises are considered in [45], and the optimal weights under the Neyman– Pearson criterion are derived.”
- Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks (2013, methodology): “In [11], a softened hard fusion scheme with two-bit overhead for each CR device is considered.”
- A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions (2010, methodology): “A fusion scheme based on multiple-bit decisions is shown in [41].”