Beamforming-Codebook-Aware Channel Knowledge Map Construction for Multi-Antenna Systems

2025-05-22

AI Summary

Traditional channel knowledge maps (CKMs) in MIMO systems suffer from insufficient accuracy for beamforming and precoder selection, coupled with high modeling complexity. To address this, we propose a codebook-aware CKM construction method. Our approach explicitly incorporates DFT-based precoding vectors into the CKM generation framework and introduces TransUNet—a hybrid architecture that synergistically combines UNet’s multi-scale local feature extraction with Transformer’s global linear modeling capability—enabling electromagnetic-environment-driven end-to-end CKM regression. Experimental results demonstrate a 17% reduction in root-mean-square error (RMSE) over the state-of-the-art RadioWNet. The method supports real-time CKM generation and is publicly available as open-source code.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neural networks (NNs) have achieved efficient CKM generation with reduced computational complexity and real-time processing capabilities. Nevertheless, existing research predominantly focuses on single-antenna systems, failing to address the beamforming requirements inherent to MIMO configurations. Given that appropriate precoding vector selection in MIMO systems can substantially enhance user communication rates, this paper presents a TransUNet-based framework for constructing CKM, which effectively incorporates discrete Fourier transform (DFT) precoding vectors. The proposed architecture combines a UNet backbone for multiscale feature extraction with a Transformer module to capture global dependencies among encoded linear vectors. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) deep learning (DL) approaches, yielding a 17% improvement in RMSE compared to RadioWNet. The code is publicly accessible at https://github.com/github-whh/TransUNet.

Problem

• Constructing beamforming-aware CKM for multi-antenna MIMO systems
• Overcoming computational limitations of traditional CKM methods
• Enhancing MIMO precoding vector selection via TransUNet framework

Innovation

• TransUNet framework for CKM construction
• Integrates DFT precoding for MIMO systems
• Combines UNet and Transformer for feature extraction