2 papers
eess.SP2026
Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling
Ruining Fan, Xingyu Huang, Mouli Chakraborty +2
The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its…
eess.SP2025
Quantum Deep Learning for Massive MIMO User Scheduling
Xingyu Huang, Ruining Fan, Mouli Chakraborty +2
We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addr…