## 4 Simulation and Test Results
Since the pre-trained signal patterns are used in this system; the Shannon boundary is seen as it is virtually broken at some points (G\({}_{4}\) and G\({}_{6}\)) in SNR _vs._ LSE space. This depends on the difference between the bandwidth of recovered digital data based on priori information in glossary and the raw physical data bandwidth inside the communication medium. In other words, use of predefined patterns in glossaries also provides a sort of soft compression of the information and virtually working beyond the Shannon boundary depends on the nature of data and the selected glossary features. In fact, since a sort of error recovery is also processed during the decoding of the received signal by a trained ANN, it cannot be seen as violation of the physical limitation set by Shannon Theorem. Another observation is that the PBCS is robust to high levels of Additive White Gaussian Noise (AWGN). Figure 9 represents the distortion effect of the AWGN on the signal patterns for the selected SNR values. The pre-trained ANN can recognize the pattern under this noise pressure. This distortion on one of the glossary pattern G\({}_{1}\) is shown in Figure 8. The data is recovered under \(-2.12\) dB SNR value with \(10^{-5}\) BER. The comparison between the _E\({}_{b}\)/N\({}_{0}\)_ curves of the PBCS for specific glossaries and of the MF performance is given in the Figure 10.  
According to the performance evaluation, the proposed method gives better results than the MF for specific glossaries. This figure shows that G\({}_{4}\) has 28.2% and G\({}_{2}\) has 92.2% better performance than MF. These values are derived from the area calculation under the graph between ultimate Shannon limit (\(-1.59\) dB) and 10 dB in Figure 10. SNR level affects the _E\({}_{b}\)/N\({}_{0}\)_ value exponentially, although the  
Figure 8: LSE via manageable SNR and comparison with Shannon boundary. LSE is linear dependent. Therefore, the slope of the curve takes different positions regarding to the position of the glossary in SNR _vs._ LSE space. It must be considered that besides the use of fixed signal pattern sets (Glossary) for each graph in simulation results, performance optimization of the PBCS mainly depends on adaptive switching between set of patterns due to SNR capability and the sensed SNR under dynamic requirement and availability variations. MF has better performance than G4 for high BER values when the comparison is made for fixed glossary set. Figure 10: Performance comparison between PBCS and matched filtering. Figure 9: The distortion on signal pattern with AWGN in Glossary 1 (G1). A block diagram of a semi-hardware lab test platform is shown in Figure 11. This test platform is independent of RF front-end features and designed to confirm assumptions at baseband only. ANN and the calculations are performed on a PC connected via I/O cards. Some glossaries constructed with SPEC algorithm for 8 KHz central frequency are listed in Table 2. These glossaries are used to test baseband performance of PBCS. Randomly generated binary information is encoded here by the PBCS encoder. The base frequency is selected as 8 kHz for the principle validation. The spectral bandwidth utilization is measured through a spectrum analyzer. Each pattern in baseband is sampled by 40 kHz Analog to Digital Converter (ADC). The digital information is fed into ANN by the use of Delay Network. ANN decodes the digital signal to binary words and the sequencer concatenates the ANN output. The comparator measures the BER with respect to buffered original and the information recovered by the PBCS receiver. LSE values for three different glossaries at three different SNR rates are listed in Figure 7:0  
In real-time applications, multi-path effect is an important factor that is effective in both RF communication and underwater ultrasonic communication. Although we have developed solutions by