Quantum-Annealing-Aided Multi-User Detection in Overloaded MIMO-OFDM Systems
Kouki Yonaga, Kenichi Takizawa
We present performance evaluation of quantum-annealing-aided multi-user detection (QA-aided MUD) in over-loaded and fully loaded multi-input multi-output systems with orthogonal frequency division multiplexing (MIMO-OFDM). QAaided MUD is a method of iterative multi-user detection (MUD) accelerated by using QA. We previously applied this method to single-input single-output (SISO) single-carrier non-orthogonal multiple access (SC-NOMA) and demonstrated that it achieves error rate and convergence performance comparable to the conventional iterative MUD based on the MAP algorithm. In this study, we have newly developed QA-aided MUD for MIMO-OFDM and conducted performance evaluation under the 3GPP UMa channel model by employing the D-Wave qquantum annealer and other digital annealing techniques. The evaluation results show that the block error rate (BLER) obtained by QA-aided MUD outperforms that of conventional low-complexity MUDs, particularly in overloaded scenarios. It is also observed that the annealing parameters significantly impact BLER in some conditions. Additionally, we present bit error rate (BER) analysis using a few instances, employing both the D-Wave quantum annealer and other digital annealing techniques. The results demonstrate that QA-aided MUD with each annealing technique avoids the error floor and can achieve error-free performance. Finally, we discuss future perspectives by evaluating the computational complexity and computation time of QA-aided MUD.
IEEE Vehicular Technology Conference•Jun 17, 2025•2 citations