ANALYSIS OF METHODS FOR ENHANCING SPECTRAL EFFICIENCY IN INFORMATION SYSTEMS

This article is devoted to the study of the physical properties of deterministic, chaotic, and stochastic signals used in modern information systems with the aim of enhancing their spectral efficiency. Special attention is given to chaotic and stochastic signals, which, due to their unique structures, ensure a high level of information security. However, they require further development to achieve optimal utilization of the frequency spectrum. The study addresses a pressing issue in communication systems: the need to balance high spectral efficiency with robust data protection and interference resilience. Deterministic signals, such as harmonic signals, traditionally used in narrowband systems, are characterized by simplicity and predictability but suffer from low spectral efficiency and poor resistance to interference. In contrast, chaotic and stochastic signals exhibit significant advantages in terms of security and interference immunity but require advanced signal processing techniques to overcome challenges associated with their wide spectral bandwidth and energy consumption. The research methodology integrates mathematical modeling and signal analysis. Traditional approaches, such as Fourier transformation, wavelet transformation, and Hilbert transformation, are compared with innovative techniques, including Volterra series and Karhunen–Loève transformation. The comparative analysis is based on evaluating spectral efficiency, interference resilience, and the energy requirements of different signal types. The results demonstrate that chaotic signals outperform deterministic and stochastic signals in terms of spectral efficiency across broader frequency ranges. However, chaotic signals require more sophisticated processing methods to ensure their stability and reliability in modern communication systems. Stochastic signals, while offering superior interference resistance and information security, exhibit lower spectral efficiency due to their broad frequency spectrum and uneven energy distribution. Innovative approaches, such as Volterra series and Karhunen–Loève transformation, significantly improve the spectral efficiency of chaotic and stochastic signals by reducing redundancy and optimizing frequency utilization. These findings highlight the need for integrating advanced signal processing methods into information systems to enhance their performance and reliability. The study's results have practical implications for the development of advanced communication systems, such as cellular networks, the Internet of Things, and satellite communication systems, where high data confidentiality and efficient spectrum usage are critical.