Wireless communication is being used in all communication standards. However, with each passing day, the
bandwidth scarcity has become a significant concern for the upcoming wireless technologies. In order to address this
concern, various techniques based on artificial intelligence have been designed. The basic intelligent radio called
cognitive radio, has been devised. It works on the basic principle of spectrum sensing and detecting the free frequency for
transmission of the secondary user, who is an unlicensed user. This work proposes an efficient technique that has been
developed to design cognitive radio based on SDR platform. The frequency updating algorithm has been added for the
performance assessment of the proposed technique. The analysis posits that for every 10dB rise in Gaussian Noise, the bit
error rate of secondary transmitter and spectrum sensor, cause an increment of 19.59% and 29.39% respectively. It has
been found that spectrum sensor is more prone to noise and that the Gaussian noise degrades the performance of the
system. Therefore, it is pertinent that the spectrum sensor should be programmed carefully. This analysis, shows that the
best range of spectrum sensor under Gaussian noise is 0 to 0.1dB. and the bit error rate is within this specified range.
Background:
Used the GNU radio companion software on software defined radio platform.
Objective:
To design the primary user and secondary user using GNU radio and to design an algorithm to update the
frequency.
Methods: Designing both the transmitter and receiver in different laptops using GRC. Python code for updating algorithm
is written at the back end.
Results:
Performance is increased in making the intelligent in bit error rate as well as the transmission rate.
Conclusion:
Various parameters measured for cognitive radio which makes it more efficient in spectrum sensing.
Cyclostationarity-based versus eigenvalues-based algorithms for spectrum sensing in cognitive radio systems: Experimental evaluation using GNU radio and USRP