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

2026-01-23

AI Summary

This work addresses the challenges of high computational complexity and difficulty in modeling long-range dependencies in channel estimation for OFDM systems with a large number of subcarriers. To this end, the authors propose a neural network architecture that integrates self-attention mechanisms with a customized Mamba framework. By incorporating a bidirectional selective scanning mechanism, the model effectively captures non-causal, long-range dependencies among subcarriers. The proposed approach achieves superior channel estimation performance compared to existing Transformer-based and baseline neural networks, while significantly reducing both model parameter count and spatial complexity. Its efficacy and accuracy are validated under the 3GPP TS 36.101 channel model, demonstrating its potential for practical deployment in large-scale OFDM systems.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Learning & Optimization for NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• channel estimation
• OFDM
• subcarriers
• low complexity
• long-distance dependencies

Innovation

• Mamba architecture
• channel estimation
• self-attention mechanism
• bidirectional selective scan
• OFDM