Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View

By Edward Chu Yeow Peh, Ying-Chang Liang, Yong Liang Guan, Yonghong Zeng. IEEE Transactions on Vehicular Technology, 2009

490 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/698b7261b4bb888c907aff82293e27adf41507d2

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

1. Geoffrey Y. Li
2. Ying-Chang Liang
3. Brandon F. Lo
4. Ravikumar Balakrishnan
5. Ian F. Akyildiz
6. Ekram Hossain
7. Kwang-Cheng Chen
8. Lu Lu

In Electrical and Electronic Engineering

1. Geoffrey Y. Li
2. Ying-Chang Liang
3. Ekram Hossain
4. Brandon F. Lo
5. Ravikumar Balakrishnan
6. Ian F. Akyildiz
7. Lu Lu
8. Rui Zhang

Citations in the corpus

• Ten years of research in spectrum sensing and sharing in cognitive radio (2012, methodology): “For the K -out-of- N rule, the optimal value of K and sensing time are obtained in [52] by maximizing the average achievable throughput of the SU system subject to a detection performance requirement.”
• Cooperative spectrum sensing in cognitive radio networks: A survey (2011, methodology): “In [63], the sensing-throughput tradeoff is formulated as an optimization problem in cooperative sensing aiming to maximize the average CR throughput under the presence and the absence of PUs, subject to the constraint of detection probability for PU protection.”
• Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network (2018, background): “1Although we do not take into account the spectrum sensing [11]–[13] due to the consideration of underlay CRN, the integration of our proposed scheme with the spectrum sensing in the interweave CRN is also interesting and we have left this integration as a future work.”
• Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks (2013, methodology): “For example, the OR-rule [8], the AND-rule, the counting rule [9], and the linear quadratic combining rule [10] are commonly used for CSS.”
• Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “Second, hard combining [156][157], in which CR users make a local decision and transmit the one-bit decision for hard”
• Cognitive radio networking and communications: an overview (2011, background): “studied in [92] by considering such sensing-access tradeoff and”