Harnessing Monotonic Neural Networks for Performance Prediction and Threshold Determination in Multichannel Detection

Abstract

Despite extensive research on numerous multichannel detection methods, predicting their performance remains difficult due to the high dimensionality of raw data and the complexity of the detection process. To tackle this, we introduce a special type of neural network designed to predict detection performance under specific environmental conditions. We utilize a monotonic neural network (MNN) to develop PdMonoNet, which ensures that the influence of input parameters on the output probability of detection is monotonic. This approach also facilitates the determination of thresholds. We provide a theoretical analysis of the universal approximation capabilities and prediction error of the network architectures we employ. Numerical experiments conducted on both synthetic datasets and real-world scenarios within the context of multichannel spectrum sensing demonstrate the effectiveness and robustness of PdMonoNet in predicting detection performance and determining thresholds.