Deep Learning Based Channel Extrapolation for Dual-Band Massive MIMO Systems

2026-01-11

AI Summary

This work addresses the challenges of channel state information (CSI) estimation in millimeter-wave massive MIMO systems, where high-dimensional CSI and severe path loss lead to excessive pilot overhead and low signal-to-noise ratio (SNR). To overcome these limitations, the paper proposes a Multi-Domain Fusion Channel Extrapolator (MDFCE), which innovatively integrates a Mixture-of-Experts (MoE) architecture with multi-head self-attention mechanisms. Leveraging deep learning, MDFCE efficiently extrapolates millimeter-wave CSI from sub-6 GHz CSI, enabling accurate modeling of the nonlinear cross-frequency mapping. The proposed method significantly reduces pilot overhead while consistently outperforming existing approaches across diverse antenna configurations and SNR conditions, achieving both higher extrapolation accuracy and improved computational efficiency.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Multi-modal Vision
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Future wireless communication systems will increasingly rely on the integration of millimeter wave (mmWave) and sub-6 GHz bands to meet heterogeneous demands on high-speed data transmission and extensive coverage. To fully exploit the benefits of mmWave bands in massive multiple-input multiple-output (MIMO) systems, highly accurate channel state information (CSI) is required. However, directly estimating the mmWave channel demands substantial pilot overhead due to the large CSI dimension and low signal-to-noise ratio (SNR) led by severe path loss and blockage attenuation. In this paper, we propose an efficient \textbf{M}ulti-\textbf{D}omain \textbf{F}usion \textbf{C}hannel \textbf{E}xtrapolator (MDFCE) to extrapolate sub-6 GHz band CSI to mmWave band CSI, so as to reduce the pilot overhead for mmWave CSI acquisition in dual band massive MIMO systems. Unlike traditional channel extrapolation methods based on mathematical modeling, the proposed MDFCE combines the mixture-of-experts framework and the multi-head self-attention mechanism to fuse multi-domain features of sub-6 GHz CSI, aiming to characterize the mapping from sub-6 GHz CSI to mmWave CSI effectively and efficiently. The simulation results demonstrate that MDFCE can achieve superior performance with less training pilots compared with existing methods across various antenna array scales and signal-to-noise ratio levels while showing a much higher computational efficiency.

Problem

• mmWave
• Channel State Information
• Massive MIMO
• Pilot Overhead
• Dual-Band

Innovation

• Channel Extrapolation
• Massive MIMO
• Deep Learning
• Multi-Domain Fusion
• Mixture-of-Experts