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This paper introduces ChannelMamba, a novel end-to-end architecture for channel state information (CSI) prediction in 6G massive MIMO IoT systems, addressing the limitations of Transformers in handling high-dimensional, long-sequence channel data. ChannelMamba leverages a dual-domain input module processing both frequency-domain CSI and delay-domain CIR data, a cross-path parameter-sharing strategy for Mamba modules, and a bidirectional Mamba module with lightweight attention for cross-feature modeling. Experimental results demonstrate that ChannelMamba achieves state-of-the-art performance in channel prediction accuracy, robustness, generalization, and computational efficiency compared to existing methods.
ChannelMamba slashes computational costs for 6G channel prediction while boosting accuracy, thanks to a Mamba-driven architecture that efficiently models long-range dependencies in high-dimensional channel data.
Accurate channel state information (CSI) prediction is essential for 6G massive multiple-input multiple-output (m-MIMO) IoT systems. Deep learning models, such as Transformers, exhibit quadratic computational complexity, resulting in significant efficiency bottlenecks when processing high-dimensional, long sequences channel data in high-mobility scenarios. The Mamba architecture, distinguished by its unique selective state-space model (SSM), presents a promising solution which combines linear computational complexity with robust capabilities for modeling long-range dependencies. Building on this foundation, we propose ChannelMamba, an end-to-end model specifically designed for channel prediction. First, the model employs a dual-domain input module that captures comprehensive channel features by concurrently processing frequency-domain CSI and delay-domain channel impulse response (CIR) data. Subsequently, we develop a cross-path parameter-sharing strategy for the Mamba modules to efficiently capture temporal channel dynamics while enhancing model generalization. Furthermore, to address the multi-dimensional dependencies and global context inherent in channel data, we design a bidirectional Mamba module for cross-feature modeling, enhanced with a lightweight attention mechanism. Finally, extensive experimental evaluations across various standard scenarios demonstrate the significant advantages of ChannelMamba over baseline methods in terms of prediction accuracy, robustness, generalization and computational efficiency, achieving new state-of-the-art performance in channel prediction tasks.
