Intelligent Spectrum Sensing for NOMA Systems: A Cost-Sensitive LightGBM Approach with Objective-Driven Learning

In NOMA-enabled CR systems, superposed PU signals with unequal power levels and independent activity significantly complicate spectrum sensing and channel state discrimination. To address this issue, ML-based sensing exploits spectrum-domain features to perform channel state classification. However, the ML-based methods remain limited under independent PU activity and suffer from the performance tradeoff issue since the spectrum sensing constraints are not explicitly incorporated into the learning process. In this paper, we propose an OCL method that aligns LightGBM multiclass training with spectrum sensing objectives and leverages eigenvalue-based features to capture discriminative signal patterns under dynamic NOMA transmission. The cost-sensitive learning strategy is used to guide the classifier while the objective-driven tuning is used to optimize hyperparameters toward spectrum sensing objectives. To evaluate the overall performance toward Pd and Pfa, we propose an overall sensing ability score by adopting the SPOTIS method. As a result, the proposed OCL method achieves the highest overall sensing ability scores with an average score of 0.638, outperforming EBSS-RF at 0.610 and FBSS-LR at 0.221. Under challenging signal pattern discrimination conditions, the OCL method improves the overall sensing ability score by 6.26% and 0.9 under different power coefficients compared to EBSS-RF, highlighting its effectiveness in addressing the performance tradeoff issue.