Cyclostationarity is a feature of detection based on the blind approach for Spectrum Sensing and classification. Here is a Spectrum Sensing (SS) device. The main function of this device is that it should be able to detect the presence of any signal over noise regardless of its location in the area.
The other main function is that it should be able to identify and differentiate all the signals received from it correctly. Here, the tasks to be performed by them should be with very little or no prior information.
They should be able to evaluate and know all the incoming signals or channel noise beforehand. If they do it by themselves, we can know that the machine or the feature is working properly.
Various uses of Cyclostationary feature detection
The Cyclostationarity Feature Detection (CFD) is mainly used to detect the primary users (PU). This is done by using the periodicity in the autocorrelation of all the modulated signals that are available there. Here the machines perform according to certain types of algorithms that are given to them.
These algorithms try to differentiate between a signal and a noise-based by studying all the uncorrelated nature of noise. The Cyclostationary Feature Detection is generally considered as a semi-blind approach by a lot of scientists. This is because it usually requires an amount of prior information about the type and time of the PU signal for their detection.
Here for the identification and the classification of PU signals, all the existing algorithms in the machine generally use the CFD and all the neural networks available there. Here this paper proposes a novel algorithm to get completely blind detection performance. The performance which will be hot will be based on cyclostationary feature detection.
Classifications of Cyclostationarity Feature Detection
Here the classification of the PU signals is generally based on all the basic types of statistics available regarding the cyclic spectrum. After that, an algorithm is formulated. This is done to identify the modulation scheme of the signal that is detected by the machine.
After that, all they do is classify the signal that is got without using any type of training algorithm. Here this proposed approach is also capable of detecting all the PU very effectively for SNR. This signal that the machine can detect can be as low as -8 dB. The machine can get this signal with no prior information about any kind of PU or noise in the channel.
Significance of Cyclostationarity Feature Detection
Human society has seen the recent advancement of wireless communication. This is an emerging business. These emerging wireless multimedia applications and machineries have led to a huge demand for the use of radio spectrum in many fields of work that are available nowadays. Nowadays, we all can see spectrum is scarce. This scarcity of spectrum has become one of the major problems creating problems or becoming drawbacks in the growth of wireless communication worldwide. Here there are different solutions to tackle these kinds of problems.
These may include the Free Space Optical (FSO)communication and Cognitive Radio (CR). These two types of networks have been used for the efficient as well as easy use of spectrum. This cyclostationary feature can be a promising solution provided by the concept of dynamic spectrum access.
Here the main advantage is that the users intelligently sense the spectrum, and then all they do is use the vacant bands. After that, the machine can also get this signal with no prior information about any kind of PU or noise in the channel, which is a major feature for any user.
Conclusion
This is an intelligent radio and a network technology that can detect all the spectrum bands available to the user. After that, all they do is adjust its transmission parameters accordingly, which will benefit the user.
Moreover, in some wireless applications and devices, there is a feature that requires correctly identifying and then classifying all the signals received with very little or no prior information about any kind of incoming signal or channel noise. Here all they have done is that they have developed a lot of different techniques to use them so that they can overcome all the hardships that come with it.
Read More : What Are Challenges of Cyclostationary Feature Detection?