Abstract
Problem Statement: Pattern Based Communication System (PBCS) aims to handle two prominent restrictions in communication lines. These are the inefficient utilization of the bandwidth under the spectrum limitations and sensitivity of the data transmission rate to the variation of Signal to Noise Ratio (SNR). Approach: The key point of PBCS is to construct the optimal communication signals, which consist of patterned data that can be recovered by the cognitive receiver, for a successful data transmission even in low SNR and high data bit rate. Result: In this study, performance of the PBCS is evaluated by the comparison with Matched Filtering (MF). Conclusion: This comparison shows that transmission performance of the PBCS is higher than MF due to its dynamic and flexible operation capability under variation of spectral conditions.
Highlights
There are two crucial aspects those define fundamental restrictions in data transmission channels
Each glossary in the glossary space is indexed regarding to classification of two components: The transmission capability under the specified noise suppression in terms of Signal to Noise Ratio (SNR) and Link Spectral Efficiency (LSE), which consists of the utilized spectral bandwidth (Hz) by the test signal pattern and its data bit rate
As proposed by the Shannon Limit Theorem (Eq 1) (Shannon, 1948), the channel capacity is directly proportional to the spectral bandwidth and SNR
Summary
There are two crucial aspects those define fundamental restrictions in data transmission channels. In PBCS, the transmitter uses the free SNR capacity and the free spectral bandwidth of communication channel, dividing the data bit rate to the utilized spectral bandwidth to come up with the LSE These two values correspond to a specific glossary in the glossary space. Function to perform Multi-layer Perceptron (MLP) to sense the spectrum As opposed to these methods, PBCS develops its own modulation technique by using Amplitude Frequency Phase Shift Keying (AFPSK) and the data communication is achieved when the signals constructed through this method are recognized by ANN. The ANN is trained offline according to the glossary patterns, which are constructed by SPEC algorithm These signal patterns are distorted in the communication medium and they are called to the test set.
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