QNN framework based multiclass classification for downlink NOMA detectors

Quantum neural networks (QNNs) have attracted significant attention recently, primarily because of their potential to address complex problems deemed difficult for traditional computational methods. This study explores the viability of QNN in handling multiclass classification tasks in downlink nonorthogonal multiple access (NOMA) frameworks. The investigation includes a design of QNN framework and performance evaluation of a QNN-based NOMA detector, integrating maximum likelihood (ML), successive interference cancellation (SIC), and rotated ML (RML) methods. A QNN framework was configured for all three detectors, and a comparative analysis was conducted in terms of loss, accuracy, and testing across varied signal-to-noise ratio (SNR) levels and power allocation coefficients, considering NOMA-specific characteristics. Furthermore, the computational complexity of each detector was analyzed within the proposed framework.