Adaptive Clustering Algorithm for Cooperative Spectrum Sensing in Mobile Environments
2018
In this work we propose a new adaptive algorithm for cooperative spectrum sensing in dynamic environments where the channels are time varying. We assume a centralized spectrum sensing procedure based on the soft fusion of the signal energy levels measured at the sensors. The detection problem is posed as a composite hypothesis testing problem. The unknown parameters are estimated by means of an adaptive clustering algorithm that operates over the most recent energy estimates reported by the sensors to the fusion center. The algorithm does not require all sensors to report their energy estimates, which makes it suited to be used with any sensor selection strategy (active sensing). Simulation results show the feasibility and efficiency of the method in realistic slow-fading environments.
Keywords:
- Mathematical optimization
- Adaptive algorithm
- Statistical hypothesis testing
- Cluster analysis
- Control engineering
- Computer science
- Communication channel
- Signal-to-noise ratio
- Artificial intelligence
- Energy (signal processing)
- Cascading Style Sheets
- Pattern recognition
- Real-time computing
- Energy level
- Fusion center
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