Enhancing Antenna Array Fault Detection Using Regularized Logistic Regression Techniques

The current generation communication systems can hardly afford faulty antenna arrays, let alone reliable ones, since even negligible flaws can signification affect the performance of such systems. This research proposes a new method in antenna array fault detection and diagnosis, which employs the tools of regularized logistic regression. More precisely, L1 (Lasso) and L2 (Ridge) penalties of the regularized learning algorithms are used to increase the detection accuracy and, at the same time, to decrease the level of overfitting significantly. The proposed methodology relies on a large dataset that covers possible fault conditions, including many environmental and operational factors. Sophisticated approaches are used to document significant signal properties; step by step hyperparameter optimization is carried out to enhance the performance of the logistic regression models. The experimental findings show that by applying regularized logistic regression to the fault detection criterion, much higher accuracy and fault classification robustness can be obtained compared to the traditional fault detection methods. L1 and L2 regularization are also helpful to increase the model's ability for prediction and simultaneously, they also help in understanding the significance of various features in answering to the problem in question. The findings of this research work will create the basis for the adaptation of time-based fault detection and diagnosis schemes that can adequately function in various settings, which will further guarantee the dependability as well as efficacy of antenna arrays in crucial communication networks.