Incremental Coding Testing and LT-Net Bit Error Prediction for Aircraft Pod LVDS Links

Aircraft pod Low-Voltage Differential Signalling (LVDS) links frequently suffer from transmission errors in adverse environments, compromising reliability. We propose a comprehensive ‘real-time detection—precise prediction—dynamic adaptation’ solution. Firstly, a testing system based on the Xilinx Artix-7 Field Programmable Gate Array (FPGA) was developed using incremental coding, verified across diverse hardware with quantitative physical parameters. Secondly, a Long Short-Term Memory (LSTM)-Transformer fusion network (LT-Net) with weighted loss and dynamic regularization was designed to optimize prediction in critical high Bit Error Rate (BER) regimes. To address distribution drift, an online adaptive mechanism utilizing Elastic Weight Consolidation (EWC) was integrated. Results show LT-Net reduces Mean Squared Error (MSE) by 41.7% and maintains superior Mean Absolute Error (MAE) compared to baseline Transformers, with drift-induced degradation kept within 8%. With an inference latency under 0.28 s, the system meets hard real-time requirements for aircraft pod reliability in complex scenarios.