Cognitive Radio Network with Wideband Spectrum Sensing and Reliable Data Transmission
G P Aswathy, K. Gopakumar · 2018
Spectrum sensing is an important task for cognitive radio. One major challenge for future cognitive radio networks is the sampling rate bottleneck associated with wideband spectrum sensing. In this work, we propose a novel wideband spectrum sensing technique using multicoset sampling and orthogonal matching pursuit algorithm. Multicoset sampling is a sub-Nyquist sampling technique that can be used to reduce the implementation complexity associated with the Nyquist sampling based wideband sensing techniques. Orthogonal matching pursuit is a greedy pursuit algorithm used to recover the unknown spectral support of compressive sensed sparse signals. The proposed method not only reduces the complexity associated with Nyquist sampling based systems but also provides a reliable detection capability even at low signal-to-noise ratios. Once the unknown spectral indices are identified, the vacant spectrum is allocated to the secondary users. Since the vacant spectrum is always prone to primary user interference, the secondary user uses the low-density parity-check codes for reliable data transmission. We also evaluate the performance of the system using bit-error rate analysis.