## 2 PBCS Model  
When the signal from the transmitter reaches the receiver, it is distorted by both natural and man-made noises acting upon it during the transmission process. In the proposed PBCS, the communication task is fulfilled, when, in the receiver, the distorted signal patterns are matched with the original patterns by the ANN, which is trained _a priori_ and offline. The fundamental reason for selecting ANN as the recognition layer of the system is the sensitivity of ANN while recognizing the amplitude, frequency and phase changes in the communication signal. The primary aim of PBCS is to separate the glossary patterns by using this feature of an ANN. From the CR perspective, the PBCS has the objective of optimizing the SNR _vs._ LSE relationship of the communication system by making dynamic band utilization through pre-defined glossaries, which have distinct characteristics. A prominent subject under the CR concept is spectrum sensing, on which extensive work has been reported in the literature. This work includes Matched Filtering (MF) [3], Waveform-based sensing, Cyclostationary-based Sensing [4; 5; 6], Energy Detector-based Sensing [6] and Radio Identification [7; 8; 9]. Among these, MF is the closest method to PBCS. The most significant disadvantage in the practical use of MF is the fact that the information about bandwidth, operating frequency, modulation type and order, pulse shaping, frame format _etc._ should also be known by the receiver, whereas the proposed system overcomes this disadvantage, since the information is embedded into the ANN recognition process. There are various methods that utilize ANN to achieve spectrum sensing. [PERSON] _et al._[9] have managed to successfully determine the bandwidth and its shape by using RBNN (Radial-based Neural Network). In another study, [PERSON] _et al._[10] have used Feed-Forward Back Propagation Neural Networks (FFBPNN) and Support Vector Machine (SVM) with Radial Bases Function (RBF) to make time-frequency analysis. On the other hand, [PERSON] _et al._[11] have introduced and evaluated the learning schemes those are based on artificial neural networks and their study could be used to predict the capabilities (e.g., data rate) of a specific radio configuration. Finally, [PERSON] _et al._[12] have used Spectral Correlation Density (SCD) and Spectral Coherence Function (SCF) to perform Multi-layer Perceptron (MLP) to sense the spectrum. As opposed to these methods, the proposed system develops its own modulation technique by using Amplitude Frequency Phase Shift Keying (AFPSK) and the data communication is achieved when the signal waveforms constructed through this method are recognized by MLP. The aim of this study is to evaluate the robustness of the proposed system against to the noise model in the communication channel. It is assumed that the effects of other communication channel models are handled by conventional methods in the literature. For instance, the multipath channel model effect on the performance is handled by the time delay estimation algorithm by using ANN [13]. The channel capacity determines the LSE and the measured noise level of the assigned channel points SNR value on the communication band. To select the most appropriate glossary from the glossary space, PBCS uses LSE to obtain from the data transfer rate availability in the channel capacity. Then, it measures the noise level of the assigned channel and it points SNR value on the communication band. Using these two values, it chooses the most appropriate glossary (\(G_{i}\)) from the glossary space (\(G\)) for transmission. Since the glossary space is known by both the transmitter and receiver, the received glossary information functions as the switch between ANN's at the receiver side. The ANN decodes the information and offers it to the user service. PBCS is designed for multi-purpose utilization as a single-user or multi-user access. Different information sources (S\({}_{\mathrm{x}}\), x = _1...n_, in Figure 1) can be located in the transmitter side.