# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Software Characteristics
#### 4.4.1 Spectrum Sensing  
The literature commonly refers to spectrum sensing as the procedure of gathering and analyzing radio data to establish the spectrum occupancy [56]. In order to detect transmitters, some well-known methods can be employed, i.e., Energy detector, Matched-filter, and Cyclostationary feature detection. These algorithms are connected with two hypotheses formally defined as [57; 58]  
\[x(t)=\begin{cases}n(t),&H_{0}\\ s(t)+n(t),&H_{1}\end{cases} \tag{1}\]  
where \(H_{1}\) indicates the presence of a licensed user, whereas \(H_{0}\) represents the null hypothesis. \(x\) represents the received signal, \(t\) is the time sample, and \(s\) and \(n\) denote the PU signal and the additive noise, respectively.  
In cases where the signal receiver has no prior information about the PU signal, the energy detector algorithm may be deployed at a low computational cost. It functions by comparing a signal sample from the data to a predefined threshold, where a value higher than the threshold suggests the presence of a primary user, whereas a value smaller than the threshold implies the lack of band utilization. The main downside of this approach is the fixed threshold value, as noise power can differ over time possibly assuming unknown  
Figure 4: Physical aspects of the Hope RF RFM23B on the **left** and the USRP B200 on the **right.** The boards are shown in different size scales. Adapted from the work in [51].  
behavior. In this case, the threshold could be surpassed by the noise power, indicating an invalid PU presence in a given spectrum band [59; 60].  
The matched-filter contrasts the current signal with previous collected samples from the same transmitter, in comparison to the energy method. It appears to be more precise, has a shorter sensing time and maximizes the ratio of signal to noise (SNR). However, the need for prior knowledge of the form of the transmitter signal restricts its feasibility only to the point where licensed users cooperate [61].  
In order to check whether or not the transmitted signal has periodicity, the cyclostation-ary feature detection adopts a spectral correlation function (SCF) [62]. Unlike the previous approaches, it helps the CR user to distinguish between noise and user signal, improving the efficiency of the algorithm in channels where there is greater noise [63]. Because of its computational complexity, perhaps the most serious drawback of this approach is the need for long processing time, an undesired characteristic for small energy consumption systems such as UAVs.  
Finally, machine learning approaches have been adopted in the literature to enhance the detection of transmitters. The authors of [64] achieved an overall success rate of over 99.50% in predicting PU presence by using an Artificial Neural Network (ANN) at different SNR frequencies. Similarly, [PERSON] et al. [65] proposed a cooperative detection device combining the energy detector with ANNs, adding a basic ANN for each SU, and a base station responsible for final decision-making, referred to as the Fusion Center. [PERSON] et al. [66] proposed a new Fuzzy logic system to adapt each spectrum scenario to the most suitable transmitter detection algorithm. The reader may refer to the works in [67; 68] for further information concerning spectrum sensing techniques.