Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems

TL;DR

This paper introduces a hybrid quantum convolutional neural network (HQCNN) designed to optimize pilot assignment in cell-free massive MIMO systems, achieving near-optimal throughput with improved efficiency over traditional methods.

Contribution

The paper presents a novel HQCNN architecture utilizing parameterized quantum circuits for efficient feature extraction and faster convergence in pilot assignment tasks.

Findings

HQCNN achieves throughput close to exhaustive search.

HQCNN outperforms existing benchmarks.

Proposed method reduces computational complexity.

Abstract

A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.