CSS (Cooperative Spectrum Sensing)

Introduction:

In modern wireless communication systems, the utilization of available spectrum has become a challenging task due to the rapid growth of wireless applications and services. One solution to this problem is the implementation of Cognitive Radio (CR) technology, which enables secondary users (SUs) to use the unused frequency bands of primary users (PUs) in an opportunistic manner without interfering with primary communication. Cooperative Spectrum Sensing (CSS) is one of the most important techniques in CR that helps in detecting PU activity in the spectrum holes. In this article, we will discuss the CSS technique and its significance in CR networks.

Cooperative Spectrum Sensing:

CSS is a technique that enables SUs to sense the presence or absence of PUs in a particular frequency band. In CSS, multiple SUs collaborate with each other to detect PU activity. CSS is an essential technique for CR networks because it allows SUs to determine the availability of spectrum bands and identify the presence of PUs in a reliable and accurate manner.

CSS can be classified into two categories: centralized and distributed. In centralized CSS, a central node, known as the fusion center (FC), collects sensing data from all SUs and makes a final decision about the presence or absence of PU activity. In distributed CSS, each SU performs sensing independently, and a final decision is made by a fusion rule that combines the sensing data from multiple SUs.

Challenges in CSS:

CSS faces several challenges due to various factors, including wireless channel fading, noise, and hidden terminal problem. These factors can cause errors in sensing, leading to false detection or false non-detection. To mitigate these challenges, several techniques have been proposed, including energy detection, matched filter detection, and cyclostationary feature detection.

Energy Detection:

Energy detection is the simplest and most widely used CSS technique. In energy detection, SUs sense the energy level of a particular frequency band and compare it with a predetermined threshold. If the energy level exceeds the threshold, it is considered that the PU is present in the frequency band, and if the energy level is below the threshold, it is considered that the PU is absent in the frequency band. Energy detection is effective in detecting PU activity when the PU signal is weak or unknown. However, energy detection has limitations in scenarios where the noise power is high, leading to a high false detection rate.

Matched Filter Detection:

Matched filter detection is a CSS technique that utilizes a known PU signal to detect PU activity. In this technique, SUs apply a matched filter to the received signal and compare the output with a threshold. If the output exceeds the threshold, it is considered that the PU is present in the frequency band, and if the output is below the threshold, it is considered that the PU is absent in the frequency band. Matched filter detection is effective in scenarios where the PU signal is known, and noise is present in the signal. However, this technique requires prior knowledge of the PU signal, which may not always be available.

Cyclostationary Feature Detection:

Cyclostationary feature detection is a CSS technique that utilizes the cyclostationary nature of the PU signal to detect PU activity. In this technique, SUs extract cyclostationary features from the received signal and compare them with a predetermined threshold. If the features exceed the threshold, it is considered that the PU is present in the frequency band, and if the features are below the threshold, it is considered that the PU is absent in the frequency band. Cyclostationary feature detection is effective in scenarios where the PU signal is cyclostationary, but it requires a higher computational cost than energy detection and matched filter detection.

Fusion Rules:

In distributed CSS, a fusion rule is used to combine the sensing data from multiple SUs and make a final decision about the presence or absence of PU activity. The fusion rule should be designed in such a way that it maximizes the detection probability while minimizing the false alarm probability. There are several fusion rules, including AND, OR, and majority voting. The AND rule declares the presence of PU activity only when all SUs detect the PU signal. The OR rule declares the presence of PU activity when at least one SU detects the PU signal. The majority voting rule declares the presence of PU activity when the majority of SUs detect the PU signal.

The performance of the fusion rule depends on the sensing data quality, the number of SUs, and the noise level. In general, the majority voting rule is the most popular fusion rule because it provides good performance while requiring a low computational cost.

Conclusion:

CSS is a critical technique in CR networks that enables SUs to detect PU activity and identify the available spectrum holes. CSS faces several challenges, including wireless channel fading, noise, and hidden terminal problem, which can cause errors in sensing. To mitigate these challenges, several CSS techniques, including energy detection, matched filter detection, and cyclostationary feature detection, have been proposed. In distributed CSS, a fusion rule is used to combine the sensing data from multiple SUs and make a final decision about the presence or absence of PU activity. The majority voting rule is the most popular fusion rule in CSS due to its good performance and low computational cost. CSS plays a significant role in enabling SUs to access the unused frequency bands of PUs in a reliable and efficient manner, thereby improving spectrum utilization and mitigating the spectrum scarcity problem in wireless communication systems.