AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation
Berkay Guler, Hamid Jafarkhani

TL;DR
AdaFortiTran is an innovative transformer-based model that significantly improves OFDM channel estimation accuracy and robustness in fast-fading and low-SNR conditions by combining local and global feature extraction with adaptive priors.
Contribution
The paper introduces AdaFortiTran, a novel adaptive transformer architecture that integrates convolutional layers, attention mechanisms, and prior information to enhance OFDM channel estimation in challenging environments.
Findings
Achieves up to 6 dB reduction in MSE compared to state-of-the-art models.
Demonstrates robustness across high Doppler shifts and low SNR scenarios.
Effectively models both local correlations and long-range dependencies in OFDM channels.
Abstract
Deep learning models for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems often suffer from performance degradation under fast-fading channels and low-SNR scenarios. To address these limitations, we introduce the Adaptive Fortified Transformer (AdaFortiTran), a novel model specifically designed to enhance channel estimation in challenging environments. Our approach employs convolutional layers that exploit locality bias to capture strong correlations between neighboring channel elements, combined with a transformer encoder that applies the global Attention mechanism to channel patches. This approach effectively models both long-range dependencies and spectro-temporal interactions within single OFDM frames. We further augment the model's adaptability by integrating nonlinear representations of available channel statistics SNR, delay spread, and Doppler…
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Taxonomy
TopicsAdvanced Wireless Communication Techniques · Error Correcting Code Techniques · Advanced Adaptive Filtering Techniques
MethodsAttention Is All You Need · Linear Layer · Multi-Head Attention · Dense Connections · Dropout · Layer Normalization · Byte Pair Encoding · Softmax · Absolute Position Encodings · Residual Connection
