Durak-Ata, "Dispersed chirp-z transform-based spectrum sensing and utilization in
cognitive radio networks," IET Signal Processing, vol.
In the area of clustering in
cognitive radio networks, there has been a lot of research work, but only a few of them considered the stability of the cluster formed.
In this paper, Multi-Antenna System with Blind Source Separation (bSS) is applied in the context of
cognitive radio. BSS provide the secondary users with the ability to eliminate the silence period during the spectrum sensing and then enhance the secondary throughput.
However,
cognitive radio has also encountered various types of security threats, as well as challenges in the networks, due to the open nature of the
cognitive radio architecture [11, 12].
For the simulation,
cognitive radio network parameters are set with S including NSUs, MUs, and FC.
As the
Cognitive Radio moniker suggests, the emphasis on these techniques is for the radio to adapt to its environment.
Cognitive radio (CR) [1], [2] has been consequently proposed to solve the inefficiency in spectrum assignments of legacy static radio.
Keywords: (SDR) Software Defined Radios,(RF) Radio Frequency, (ADC) Analog to Digital Conversions, (DAC) Digital to Analog Conversions,(DUC) Digital up Conversion,(DDC) Digital Down Conversion, (CR)
Cognitive Radio, (CRN)
Cognitive Radio Networks, (UWB) Ultra-wideband Communications,(PU) Primary User,(SU) secondary user,(SNR) Signal to Noise Ratio,(AWGN) Additive White Gaussian Noise,(PCM) Pulse Code Modulation,(FFT) Fast Fourier Transform,(MF) Match Filter.
The presence of a PU and several SUs forms a
cognitive radio network (CRN).
Abstract:
Cognitive radio networks (CRNs) have emerged as a paradigm addressing the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the unused spectrum by licensed primary users (PUs).