Distributed Compressive Spectrum Sensing Using Robust Power Estimation Techniques Under Non-Gaussian Noise
- Resource Type
- Conference
- Authors
- Bhavana, Bandaru; Sabat, Samrat L.; Namburu, Swetha; Panigrahi, Trilochan
- Source
- 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) COMmunication Systems & NETworkS (COMSNETS), 2024 16th International Conference on. :681-684 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Simulation
Estimation
Reconstruction algorithms
Cost function
Sensors
Cognitive radio
Wideband
Distributed spectrum sensing
compressive sensing
non-Gaussian noise
Maximum correntropy criterion
Huber cost function
- Language
- ISSN
- 2155-2509
Compressive wideband spectrum sensing in a cooperative cognitive radio network requires each secondary user to reconstruct the original signal from the compressed measurement. The reconstruction algorithms are computationally complex and hence overloads the processing of each secondary user. To mitigate this, we reconstruct the signal using distributed hard thresholding (DiHaT) and distributed hard thresholding pursuit (DHTP) at each secondary user in a non-Gaussian environment. Further, we propose a robust power estimation technique using (i) diffusion maximum correntropy criterion and (ii) diffusion Huber cost function of the reconstructed signal to minimize the impact of non-Gaussian noise on detection. The simulations are carried out on a multi-carrier Universal-Filtered Multi-Carrier (UFMC) signal. The detection performance of distributed robust detectors using reconstructed signal is compared with its uncompressed counterpart.