Quantum Optimization of Radio Resources for Minimal Latency Communications
Marina Gavrilovskaia, Kwang‐Cheng Chen, Albert B. Dinkins V · 2024
Open-Loop wireless communication enables minimal latency communication with the assistance of machine learning in various aspects. A typical predictive downlink radio resource access for such minimal latency communications requires real-time optimization, which is not possible for standard classical machine learning and particularly deep learning using artificial neural networks, due to computational complexity. However, quantum optimization introduces a new technology opportunity to achieve such a purpose. Turning an allocation or knapsack problem into a quadratic unconstrainted binary optimization problem, given a quantum edge processor, we employ quantum approximation optimization algorithm to accomplish with polynomial computational complexity, to successfully realize radio resource allocation and pave a new approach to 6G mobile communications of minimal latency communication.