Enhanced signal classification and feature extraction for next-generation wireless communication systems

Abstract

As wireless communication systems quickly evolve, propelled by new technologies such as 5G and the future 6G, the demand for advanced signal processing techniques that are more efficient and robust has grown significantly. This paper presents advanced techniques for signal classification and feature extraction in future wireless communication systems. This work also aims to enhance the sensitivity and specificity of signal detection in multi-network and complex, diverse environments through the application of machine learning algorithms and feature extraction techniques such as PCA. The proposed methods are evaluated on simulated pulse signals, such as Gaussian and Chirp pulses in order to demonstrate their performance in different real-world settings, i.e., IoT networks, dense communication scenarios, etc. The outcomes indicate noteworthy advancements in terms of classification accuracy, computational efficiency, and system resilience, underscoring the promise of these augmented techniques for prospective wireless communication applications. Overall, this study represents a new paradigm for communication, allowing for smarter, more adaptive approaches to information gathering.