Designing Quantum Gradient Descent Algorithm for MIMO NOMA Rate Maximization With STAR-RIS

Abstract

We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input and multiple-output systems. The QGD algorithm utilizes the principles of quantum parallelism and superposition to efficiently solve the high-dimensional optimization challenges inherent in configuring transmit and receive beamformers and STAR-RIS elements. Extensive simulations demonstrate that the QGD algorithm significantly outperforms classical optimization methods, achieving up to 49.50% and 44.88% higher data rates compared to classical gradient descent algorithms for configurations with 256 STAR-RIS elements. Furthermore, the NOMA model shows substantial improvements in sum data rate performance, with gains of 179.65% and 145.61% over space division multiple access schemes under similar frameworks.