Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling

TL;DR

This paper introduces a quantum reinforcement learning framework inspired by Grover's search to improve user scheduling in massive MIMO systems, addressing scalability and complexity issues.

Contribution

It presents a novel quantum RL approach utilizing Grover's search for efficient exploration of large scheduling spaces in mMIMO systems.

Findings

The proposed QRL method converges effectively.

It outperforms classical CNN and QDL benchmarks.

The quantum circuit design mimics RL architecture.

Abstract

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 high computational complexity, scalability, and Channel State Information (CSI) overhead. This paper proposes a novel Grover's search-inspired Quantum Reinforcement Learning (QRL) framework for mMIMO user scheduling. The QRL agent can explore the exponentially large scheduling space effectively by applying Grover's search to the reinforcement learning process. The model is implemented using our designed quantum-gate-based circuit, which imitates the layered architecture of reinforcement learning, where quantum operations act as policy updates and decision-making units. Moreover, the simulation results demonstrate that the proposed method achieves proper convergence and significantly outperforms classical Convolutional Neural Networks (CNN) and Quantum Deep Learning (QDL) benchmarks.