Sub-6GHz Assisted mmWave Hybrid Beamforming with Heterogeneous Graph Neural Network

TL;DR

This paper introduces a novel heterogeneous graph neural network that leverages sub-6GHz channel information to optimize hybrid beamforming in multi-cell mmWave networks, significantly reducing overhead and enhancing spectral efficiency.

Contribution

It proposes a new HGNN architecture with attention and residual mechanisms to effectively learn sub-6GHz and mmWave channel relationships for beamforming design.

Findings

HGNN outperforms traditional learning methods in spectral efficiency.

Attention and residual structures improve HGNN performance.

Sub-6GHz info reduces beamforming overhead.

Abstract

In next-generation communications, sub-6GHz and millimeter-wave (mmWave) links typically coexist, with the sub-6GHz link always active and the mmWave link active when high-rate transmission is required. Due to the spatial similarities between sub-6GHz and mmWave channels, sub-6GHz channel information can be utilized to support hybrid beamforming in mmWave communications to reduce overhead costs. We consider a multi-cell heterogeneous communication network where both sub-6GHz and mmWave communications co-exist. Multiple mmWave base stations (BSs) in the heterogeneous network simultaneously transmit signals to multiple users in their own mmWave cells while interfering with each other. The challenging problem is to design hybrid beamformers in the mmWave band that can maximize the system spectral efficiency. To address this highly complex programming using sub-6GHz information, a novel heterogeneous graph neural network (HGNN) architecture is proposed to learn the intrinsic relationship between sub-6GHz and mmWave and design the hybrid beamformers for mmWave BSs. The proposed HGNN consists of two different node types, namely, BS nodes and user equipment (UE) nodes, and two different edge types, namely, desired link edge and interfering link edge. In addition, the attention mechanism and the residual structure are utilized in the HGNN architecture to improve the performance. Simulation results show that the proposed HGNN can successfully achieve better performances with sub-6GHz information than traditional learning methods. The results also demonstrate that the attention mechanism and residual structure improve the performances of the HGNN compared to its unmodified counterparts.