Exploration of automated transmission control model of communication instrumentation in the context of big data

In the era of big data, with the continuous expansion of communication networks and the increasing complexity of data traffic, traditional communication instrument transmission control methods face challenges. This study aims to explore an automated transmission control model for communication instruments by integrating big data technology. A layered architecture model is designed, including data acquisition, processing, control decision - making, and application layers. A multilayer perceptron neural network - based automation control algorithm is employed. Performance tests are carried out using specific hardware and software environments. The results show that the model’s performance varies under different signal intensities, interference intensities, data types, and transmission rates. Generally, as the signal - to - noise ratio improves, the data transmission delay decreases, the bit - error rate reduces, and the system throughput increases. Different data types also affect performance due to different processing complexities.