A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future
Youness Arjoune1, Naima Kaabouch2
1School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, ND 58202, USA. youness.arjoune@ad.ndus.edu.
Sensors (Basel, Switzerland)
|January 6, 2019
Summary
Cognitive radio enables dynamic spectrum access to combat spectrum shortages. This survey details spectrum sensing advances, techniques, and future research directions for efficient radio spectrum management.
Area of Science:
- Electrical Engineering
- Computer Science
- Telecommunications
Background:
- Radio spectrum scarcity is a growing global challenge.
- Cognitive radio technology offers a solution through dynamic spectrum access.
- Spectrum sensing is crucial for effective cognitive radio operation.
Purpose of the Study:
- To provide a comprehensive survey of recent advances in spectrum sensing.
- To detail the evolution, current state, and future of spectrum sensing.
- To guide new researchers in spectrum sensing, compressive sensing, and machine learning.
Main Methods:
- Review of existing literature on spectrum sensing techniques.
- Analysis of narrowband and wideband spectrum sensing methods.
- Discussion of enabling technologies like compressive sensing and machine learning.
Main Results:
- Spectrum sensing has progressed significantly, with ongoing advancements.
- Both narrowband and wideband techniques have distinct efficiencies and limitations.
- TV white spaces represent a key application of cognitive radio.
Conclusions:
- Spectrum sensing is vital for efficient radio spectrum utilization.
- Further research is needed to overcome implementation challenges.
- Machine learning and compressive sensing are key to future network efficiency.
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