ScholarWorks@숙명여자대학교

초록

Complex-valued data, unlike real-valued data, requires consideration of intricate correlations and patterns. In particular, within learning frameworks based on real-valued computations, conventional input representations for complex numbers may fail to account for the correlations between the real and imaginary components, leading to potential inefficiencies. To address this issue, we propose a novel framework called ComplexRep, aimed at efficiently processing complex-valued data and enhancing its transparency. This framework transforms complex sequence data into a format similar to images, allowing for the consideration of inter-component correlations while improving overall model performance. The ComplexRep framework employs advanced techniques such as Information Addition and the proposed Learned Representation Integration (LRI) to strengthen low model complexity and high Initial Trial Success Probability (ITSP). Additionally, we enhance the reliability of our experiments by utilizing both public datasets and data collected from real-world environments. Extensive evaluation results demonstrate that our framework excels even under low signal-to-noise ratio (SNR) conditions, increasing the overall system efficiency. Notably, compared to previously used input formats, ComplexRep improves ITSP performance and reduces model complexity, thus proving its efficiency. Further experiments across various models confirm the framework's compatibility with several state-of-the-art models. All experiments include additional tests on real-world 5G data, validating the applicability of the proposed approach. This study presents the potential to effectively manage complex-valued data and maximize performance while offering directions for future complex-valued data processing research.

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