RadioMamba：通过混合 Mamba-UNet 打破无线电地图构建中的精度-效率权衡
RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet
AI总结：
针对无线电地图构建中的精度-效率权衡问题，提出混合Mamba-UNet架构RadioMamba，结合Mamba与卷积分支同时捕获全局与局部特征，在实现更高精度的同时运行速度提升近20倍且仅需2.9%的参数。
摘要：
无线电地图（RM）近期备受关注，因为它能为6G服务和应用提供实时且准确的空间信道信息。然而，当前基于深度学习的RM构建方法表现出众所周知的精度-效率权衡。在本文中，我们提出了RadioMamba，一种用于RM构建的混合Mamba-UNet架构以解决该权衡。通常，准确的RM构建需要建模长距离空间依赖性，以反映波传播物理学的全局特性。RadioMamba利用Mamba-卷积块，其中Mamba分支以线性复杂度捕获这些全局依赖性，而并行的卷积分支提取局部特征。这种混合设计生成了能同时捕获全局上下文和局部细节的特征表示。实验表明，RadioMamba实现了比包括扩散模型在内的现有方法更高的精度，同时运行速度几乎快20倍，且仅使用2.9%的模型参数。通过同时提升精度和效率，RadioMamba为下一代无线系统中的实时智能优化提供了一种可行方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
