Transformer-baserad mottagare för nästa generations trådlösa kommunikation

Fan Mo () och Oscar Lendrop ()

Sammanfattning

The integration of Machine Learning (ML) into various fields has prompted exploration into the effectiveness of advanced ML architectures within Orthogonal Frequency Division Multiplexing (OFDM) receivers. Prior work by NVIDIA demonstrated the promise of Graph Neural Networks (GNNs) in replacing key components of a traditional receiver, surpassing conventional methods such as Least Squares (LS) channel estimation and Linear Minimum Mean Square Error (LMMSE) combined with K-Best detection. This thesis extends that line of inquiry by investigating the Transformer architecture as an alternative base for an AI-based OFDM receiver. Various Transformer configurations are designed and optimized, and their performance is compared against classical baselines including LMMSE + Maximum Likelihood Detection (MLD), perfect Channel State Information (CSI) + MLD, and the NVIDIA GNN model. Experimental results show that the proposed Transformer-based receivers outperform both the LMMSE + MLD baseline and the NVIDIA GNN, demonstrating the potential of attention-based models in wireless signal processing.