Implementing RF and Microwave Technologies with MLXAI using Spectral Methods

Radio frequency (RF) and microwave technologies are basic units of contemporary wireless communications systems, autonomous systems, and future sensing applications. Nevertheless, when operating frequencies are raised, there are considerable problems like signal attenuation, electromagnetic interference (EMI) and thermal unstable conditions, which compromise system reliability and performance. Traditional RF optimization methods also tend to be based on the static spectral analysis or independent machine learning methods, which restricts their adaptive real-time optimization capabilities. In order to overcome this shortcoming, this paper suggests that spectral-guided machine learning and explainable artificial intelligence (MLXAI) can be proposed to address signal integrity, EMI, and allow more efficient thermal management of RF and microwave systems. Fast Fourier Transform (FFT) is used to find spectral characteristics of RF signals, which are utilized as input variables in machine learning models such as Support Vector Machines (SVM) and Neural Networks (NN) to predict signal degradation, EMI behaviour, and thermal properties of RF signals at different operational conditions. Frequency assessment with frequencies as high as 2.4 GHz up to 110 GHz shows substantial performance increases, such as improvement in signal-to-noise radiation by up to 40 percent, EMI by 50-60 percent and thermal hotspots by about 30 percent. The neural network model also had better prediction accuracy than SVM meaning that it was suitable in the modelling of nonlinear RF system behaviour. The suggested framework offers a scalable and smart system to optimize high-frequency systems in upcoming operations like 5G/6G network of communication, autonomous systems, and the development of advanced microwave circuit design.