## 4 Results
### Software Defined Radio Experimental Results
#### 4.2.1 GMSK Experiments  
For experimental purposes, a GSM PBS is configured and two PUs are connected via a phone call when PUs are active. The MCBS is configured with a basic cognitive protocol. When the signal is not detected, it will allow two SU to connect through a phone call, but if the system detects a signal with the Renyi-entropy detector, the MCBS must change the carrier frequency. If there is no other carrier frequency available, it must end the call. The flowchart of the system is shown in Figure 7.  
The parameters of SDR experiments are shown in Table 2.  
At first, there is no ongoing phone call in the MCBS and in the PBS. Then, the average noise power is measured and the noise threshold is set at \(-90\) dBm. Then, the power of the PU is measured when a phone call is taking place between two PUs connected to the PBS. As a result, the power signal is measured and the average SNR of the PU can be computed. The position of the PU is changed and the power and SNR are measured. These data are used to calculate the DFT of the signal and the Renyi  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Number of samples & 15,000 \\ Number of SU & 2 \\ Number of SU & 2 \\ Service & Phone Call \\ Distance of PU to MCBS & 0–5 m \\ Primary Signal & GSMK \\ Bandwidth & 831.8 MHz \\ Confidence level & 95\% \\ Error & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: SDR parameters for GMSK experiments.  
Figure 7: Mobile Cognitive Radio Network Flowchart.  
entropy to decide if a PU signal is present. An example of the Renyi-entropy results for noise and PU presence can be seen in Figure 8.  
The theoretical Renyi-entropy of noise in bits surpasses 2.198 [11]. According to Figure 8, the average Renyi-entropy value for noise is 2.161. Thus, the selected threshold is 2.161 implying that signals above this value are classified as noise. The averaged Renyi-entropy for the PU signal was 1.26. According to the tests, if the Renyi-entropy of the received signal is higher than 2.161, the signal is considered as noise. However, in order to be classified as a PU signal, its entropy value must be remain between 1 and 2.161. The number of detections are compared when a PU is present against the total samples and the \(\mathrm{P_{D}}\) is estimated.  
The results are compared with the simulation results as shown in Figure 9, 600 samples were taken for each SNR value. The experimental results are similar to AWGN model for urban model in an indoor environment with no obstacles.  
Figure 8: Rényi-entropy of measured PU signal.  
Figure 9: Comparison of GMSK Rényi-entropy on SDR with AWGN simulations.  
For the practical measurement of \(\mathrm{P_{D}}\), two SU remain active and the threshold is assessed. If the results of the Renyi-entropy surpass the SU signal threshold and remain under the noise threshold, then the measured signal is classified as SU. Therefore, the \(\mathrm{P_{D}}\) corresponds to how many times the SU signal is effectively detected when active and divided by the number of samples. For example, for a \(\mathrm{SNR}=-12\) dB in the case of the Renyi-entropy detector, the \(\mathrm{P_{D}}\) is above 96% (579/600 samples) and the difference with AWGN is equal to \(-3\) dB because due to fading.  
The results are compared with the results from simulations for a Rayleigh channel as is shown in Figure 10. The experimental results prove to be better for an urban model in an indoor environment with obstacles compared to the Rayleigh channel and the difference with AWGN is \(-3\) dB because of fading.