MambaNet: Mamba-assisted Channel Estimation Neural Network With Attention Mechanism
This is an incremental improvement for wireless communication systems, specifically targeting efficient channel estimation in OFDM configurations.
The paper tackles channel estimation for OFDM waveforms with many subcarriers by proposing a Mamba-assisted neural network with attention, achieving improved performance and lower complexity compared to baseline neural networks.
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.