Design and Evaluation of Neural Network-Based Receiver Architectures for Reliable Communication

This study evaluates various architectures and compares their BER and BLER performance across different noise levels. Two novel models, the Dual Attention Transformer (DA T) and the Residual Dual Non-Local Attention Network (RDNLA), integrate self-attention and residual learning to enhance signal reconstruction. These models bypass conventional channel estimation and equalization by directly predicting log-likelihood ratios (LLRs) from received signals, with noise variance as an additional input. Simulations show that DA T and RDNLA outperform traditional and other neural receiver models under varying signal-to-noise ratios (SNR), while their computational efficiency supports their feasibility for next-generation communication systems.