RadioMamba用混合架构突破无线地图构建的精度与效率瓶颈
RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet
- 采用Mamba与卷积并行结构，线性复杂度捕捉全局空间依赖
- 比现有方法精度更高，推理速度接近快20倍，参数量仅占2.9%
- 适合6G实时智能优化场景，尤其关注高效高精度地图构建的研究者
无线地图（RM）近年来受到广泛关注，因其可为6G服务与应用提供实时、精确的空间信道信息。然而，当前基于深度学习的RM构建方法普遍存在精度-效率权衡问题。本文提出RadioMamba，一种用于RM构建的混合Mamba-UNet架构以解决该问题。准确的RM构建需建模长程空间依赖，体现波传播物理的全局特性。RadioMamba采用Mamba-卷积块，其中Mamba分支以线性复杂度捕捉全局依赖，同时并行卷积分支提取局部特征，生成兼具全局上下文与局部细节的特征表示。实验表明，RadioMamba在保持较高精度的同时，推理速度比现有方法快近20倍，模型参数量仅为2.9%。该方法显著提升了精度与效率，为下一代无线系统中的实时智能优化提供了可行方案。
原文摘要 · Abstract (English)
Radio map (RM) has recently attracted much attention since it can provide real-time and accurate spatial channel information for 6G services and applications. However, current deep learning-based methods for RM construction exhibit well known accuracy-efficiency trade-off. In this paper, we introduce RadioMamba, a hybrid Mamba-UNet architecture for RM construction to address the trade-off. Generally, accurate RM construction requires modeling long-range spatial dependencies, reflecting the global nature of wave propagation physics. RadioMamba utilizes a Mamba-Convolutional block where the Mamba branch captures these global dependencies with linear complexity, while a parallel convolutional branch extracts local features. This hybrid design generates feature representations that capture both global context and local detail. Experiments show that RadioMamba achieves higher accuracy than existing methods, including diffusion models, while operating nearly 20 times faster and using only 2.9\% of the model parameters. By improving both accuracy and efficiency, RadioMamba presents a viable approach for real-time intelligent optimization in next generation wireless systems.
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