用混合Mamba-注意力架构提升大规模子载波信道估计精度。
Hybrid Mamba-Attention Neural Architecture for Channel Estimation
- 融合定制Mamba模块与双向选择性扫描，捕捉子载波间长距离依赖。
- 相比传统Transformer，参数更少、空间复杂度更低，性能更优。
- 适合需要高效高精度信道估计的5G/6G系统部署，尤其大子载波场景。
本文提出一种混合Mamba-注意力神经架构，用于正交频分复用（OFDM）波形的信道估计，尤其适用于子载波数量庞大的配置。通过集成定制Mamba模块，该框架能高效处理大规模子载波的信道估计，并有效捕捉子载波间的长距离依赖关系。不同于传统Mamba结构，本文采用双向选择性扫描，以实现信息在两个方向上的传播，因为不同子载波的信道增益本质上是非因果的。此外，通过引入Mamba减少对二次复杂度自注意力的依赖，所提方法相比全注意力架构具有更低的空间复杂度。基于3GPP TS 36.101信道模型的仿真结果表明，相较于其他基线神经网络，该方法在可比性能下参数更少，且对未见过的信道表现出良好泛化能力。
原文摘要 · Abstract (English)
This paper proposes a hybrid Mamba-attention neural architecture to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. By integrating a customized Mamba module, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike the conventional Mamba structure, this paper implements a bidirectional selective scan to enable information propagation from both directions, because channel gains at different subcarriers are inherently non-causal. In addition, by integrating Mamba to reduce the reliance on quadratic-complexity self-attention, the proposed solution achieves lower space complexity than fully transformer architectures. Simulation results based on the 3GPP TS 36.101 channel demonstrate that compared to other baseline neural networks, the proposed method achieves superior channel estimation performance with fewer tunable parameters and exhibits good generalization across previously unseen channels.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。