Quantum Machine Learning Enhanced SDR Architecture for Adaptive and Secure Wireless Communication Using OQAM

Abstract This paper presents a novel hybrid architecture that integrates Software Defined Radio (SDR) with Quantum Machine Learning (QML) for secure, adaptive, and high-performance wireless communication. The proposed system utilizes Offset Quadrature Amplitude Modulation (OQAM) for enhanced spectral efficiency and reduced inter-symbol interference, benefiting from the nonlinear signal properties managed by filter bank-based multicarrier systems. Quantum Support Vector Machines (QSVM), Quantum Reservoir Computing (QRC), and Quantum Neural Networks (QNN) are deployed to classify modulation types and manage spectrum allocation in real-time. Simulation results derived from GNU Radio and the PennyLane framework indicate that the suggested system attains a modulation classification accuracy of up to 96.3%. The QNN model trained on OQAM signals achieves an accuracy of 94.7% and an end-to-end processing latency of around 16.2 milliseconds. The design reliably sustains classification performance over 90% throughout a signal-to-noise ratio (SNR) range of 0 to 20 dB, even in the presence of noise. Secure transmission is guaranteed by a conventional control interface that enables Quantum Key Distribution (QKD), included inside the SDR environment. The integration of reconfigurable SDR hardware with nonlinear quantum machine learning algorithms positions this method as a formidable option for dynamic spectrum optimization and secure communications in forthcoming 5G and 6G networks.