MambaNet: Mamba-assisted Channel Estimation Neural Network With Attention Mechanism

• URL: http://arxiv.org/abs/2601.17108v1
• Date: Fri, 23 Jan 2026 17:26:13 GMT
• Title: MambaNet: Mamba-assisted Channel Estimation Neural Network With Attention Mechanism
• Authors: Dianxin Luan, Chengsi Liang, Jie Huang, Zheng Lin, Kaitao Meng, John Thompson, Cheng-Xiang Wang,
• Abstract summary: This paper proposes a Mamba-assisted neural network framework to achieve improved channel estimation with low complexity.<n>With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently.<n>Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation performance.
• Score: 30.651470563778503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a Mamba-assisted neural network framework incorporating self-attention mechanism to achieve improved channel estimation with low complexity for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation performance, because channel gains at different subcarriers are non-causal. Moreover, the proposed framework exhibits relatively lower space complexity than transformer-based neural networks. Simulation results tested on the 3GPP TS 36.101 channel demonstrate that compared to other baseline neural network solutions, the proposed method achieves improved channel estimation performance with a reduced number of tunable parameters.

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