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.
@article{arxiv.2601.17108,
title = {MambaNet: Mamba-assisted Channel Estimation Neural Network With Attention Mechanism},
author = {Dianxin Luan and Chengsi Liang and Jie Huang and Zheng Lin and Kaitao Meng and John Thompson and Cheng-Xiang Wang},
journal= {arXiv preprint arXiv:2601.17108},
year = {2026}
}