A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

By Yonghong Zeng, Ying-Chang Liang, Anh Tuan Hoang, Rui Zhang. EURASIP Journal on Advances in Signal Processing, 2010

750 citations recorded by the source corpus.

Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.

Subfields: Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/25cc4d987584d59fbf70eb3db0ac860d723cc741

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Networks and Communications

Citations in the corpus

Spectrum sensing in cognitive radio (2016, background): “A number of test statistics for the problem of spectrum sensing have been proposed in the literature [33, 34, 35].”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, background): “cate that all the PU transmitters are inactive at this band with a high probability. Spectrum sensing is now a very active area for research; the interested readers may refer to, e.g., [4], [5], [6], [7] for an overview of the state-of-art results in this area. As a counterpart, the SS model allows ”
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “In fact, a countable number of survey papers exist in the literature in the context of cognitive radio communications covering a wide range of areas such as spectrum occupancy measurement [13], spectrum sensing [6][7][14][15], cognitive radio under practical imperfections [16], spectrum management [”