## 4 Simulation and Test Results  
The objective of this study was to design a system which can manage SNR _vs._ LSE relationship depending on the user expectations and spectrum availabilities. In line with this objective, for each bit level, distinct glossaries were constructed and the number of neurons in the associated ANN is determined through the cross-validation process. The results of this process are given below. Figure 5: Activation Functions: (a) Sigmoid, (b) Tangent Hyperbolic, (c) Linear. The most important innovation in PBCS is to design a system, which can manage SNR depending on the user request in terms of LSE. According to the user request and channel conditions, it switches the communication signals and find out the best point. Six different glossaries are constructed with different boundary conditions for the signal parameters (A, F and P). These glossaries are constructed by the usage of SPEC algorithm for depth 6 and level \(\pm\)2. MLP is designed with single hidden layer. The number of neurons in the hidden layer is determined with the cross-validation method (Figure 7). The momentum coefficient is set to 0.5. The coefficient for the sigmoid is selected as \(\lambda=0.6\). The learning rate is adjusted in adaptive manner. The glossary space \(G\) is constructed with these rules and parameters. This matrix has six rows and each row has different number of column regarding the bit level of glossary. Some glossaries constructed with the SPEC algorithm are listed in Table 1. These values indicate the interval boundaries in SPEC space. Minimum and maximum values for each feature points -L2 and +L2 levels, respectively. Figure 6: The general view of MLP neural network architecture. P(t) is the pattern in the active glossary, and S(t) is the sampled form of the pattern. The Shannon boundary is drawn by using the Shannon limit theorem that is based on a function of signal bandwidth and the SNR rate [1] of the system. Just the parameters of this theorem are adapted to the space parameters of SNR LSE. Hence Equation (15) is derived and Shannon boundary line in Figure 8 is drawn with respect to Equation (15):  
\[\frac{C}{\mathcal{W}}=\text{LSE}=\log_{2}\biggl{(}1+10^{\left(\text{SNR}_{10} \right)}\biggr{)} \tag{15}\]  
Figure 8 shows the flexible structure of PBCS in SNR LSE space. Many distinct glossaries are constructed by using the SPEC algorithm and the AFPSK method. Constructed communication signals are then band pass filtered with respect to IEEE 802.11.a/g channel mask model [19]. According to the user demand and channel conditions, PBCS switches to the best point for optimizing the communication system performance. The marks in Figure 8 show the best working point regarding the glossary at \(10^{-5}\) BER. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Glossary** & **A\({}_{\text{min}}\) (V)** & **A\({}_{\text{max}}\) (V)** & **F\({}_{\text{min}}\) (MHz)** & **F\({}_{\text{max}}\) (MHz)** & **\(\Phi_{\text{min}}\) (\({}^{\circ}\))** & **\(\Phi_{\text{max}}\) (\({}^{\circ}\))** \\ \hline G\({}_{1}\) (1-bit) & 0.09 & 0.10 & 2410 & 2414 & –45 & +45 \\ G\({}_{2}\) (1-bit) & 0.09 & 0.10 & 2401 & 2423 & –90 & +90 \\ G\({}_{3}\) (1-bit) & 0.09 & 0.10 & 2410 & 2414 & –15 & +15 \\ G\({}_{4}\) (2-bit) & 0.09 & 0.10 & 2401 & 2423 & –90 & +90 \\ G\({}_{5}\) (4-bit) & 0.09 & 0.10 & 2410 & 2414 & –35 & +35 \\ G\({}_{6}\) (4-bit) & 0.09 & 0.10 & 2408 & 2416 & –35 & +35 \\ \hline \end{tabular}
\end{table} 
Table 1: Glossary interval boundaries for SPEC space _@_ 2.412 GHz central frequency. Figure 7: Cross-validation for determining the number of neurons. The most significant feature of the proposed method is its competence to operate at a region around Shannon boundary. This provides the manageable SNR capability by adaptive switching to optimal signal pattern (communication waveform) set between high noise redundancy and high data bandwidth requirements under observed spectrum conditions.