A Robust Signal Classification Scheme for Cognitive Radio
This work addresses the need for reliable spectrum sensing in cognitive radio systems, though it appears incremental as it builds upon existing feature-based detection algorithms.
The paper tackles the problem of robust signal classification for cognitive radio spectrum sensing by introducing a dimension cancelation method to mitigate noise uncertainty, achieving effective and robust performance validated through simulations and real-world experiments.
This paper presents a robust signal classification scheme for achieving comprehensive spectrum sensing of multiple coexisting wireless systems. It is built upon a group of feature-based signal detection algorithms enhanced by the proposed dimension cancelation (DIC) method for mitigating the noise uncertainty problem. The classification scheme is implemented on our testbed consisting real-world wireless devices. The simulation and experimental performances agree with each other well and shows the effectiveness and robustness of the proposed scheme.