NON-ORTHOGONAL SIGNAL-BASED OPTICAL COMMUNICATION SYSTEMS USING FUZZY LEARNING FOR INTERFERENCE CANCELLATION

Non-orthogonal signal-based systems are a type of communication system that uses signals that are not mutually perpendicular (i.e., not orthogonal) to transmit information. These types of systems can increase the spectral efficiency of communication systems by allowing for more data to be transmitted in the same bandwidth. Groups of signals with non-orthogonal waveforms can increase spectral efficiency, but they also increase the potential for interference. Spectrally efficient frequency division multiplexing (SEFDM) is a well-studied waveform that was originally proposed for use in wireless systems but has since found application in millimeter wave communications at 60 GHz, optical access network architecture, and long-distance optical fiber transmission. However, non-orthogonal signal-based systems are also more susceptible to interference from other sources, which can degrade the quality of the transmitted signal. To address this problem, this paper suggests using fuzzy learning techniques to cancel out interference and improve the signal-to-noise ratio. Fuzzy learning is a type of machine learning that uses fuzzy logic (FL) to handle uncertainty and imprecision in data. By using FL techniques to cancel out interference, the non-orthogonal signal-based optical communication (OC) system could potentially achieve better performance in noisy environments. Overall, this research topic has the potential to contribute to the development of more efficient and reliable OC systems that can operate in challenging environments.