A paper titled "DeepQ-MIMO: A Deep-Learned Quantum MIMO System With Rydberg Atomic Receiver in IoT" got accepted to IEEE Internet of Things Journal (IF 8.9, JCR Q1 Rank 4.1%)!
Rydberg atomic receiver has recently emerged as a breakthrough technology for sensing and communications in next-generation Internet of Things (IoT) owing to its potential to surpass the sensitivity limits of classical radio frequency (RF) receivers. In this letter, we consider a multiple-input–multiple-output (MIMO) system with an RF transmitter and a Rydberg atomic receiver. Unlike prior works, a key technical innovation of our approach lies in the joint optimization of both transmit and receive processing techniques, along with the design of reference signal injection according to a mean square error (MSE) criterion for signal recovery. However, the design problem is nonconvex on account of phase information loss in the received signal and nonlinearity of the objective function. To overcome this tricky challenge in an effective and intelligent manner, we propose a novel and high-performing deep learning (DL) framework called DeepQ-MIMO based on the construction of an advanced DL network with innovative customization mechanisms. Numerical results confirm the supremacy and efficacy of the proposed DeepQ-MIMO system, and further provide useful design insights.
