A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

Entropy

Berk Ustundag¹ 

关键词: cognitive radio;    pattern recognition;    spectrum management;    noise immunity;    neural network;   

DOI  :  10.3390/e13010064

来源: mdpi

【 摘 要 】

Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR), are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS) is an adaptive and perceptual communication method based on a Cognitive Radio (CR) approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE). The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms) depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN) in the receiver site.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.