Communication Synchronization and Anti-Interference Technology based on Artificial Intelligence

With the development of the Internet of Things and the Industrial Internet, more and more devices require stable and reliable wireless connections. When traditional communication synchronization methods encounter complex electromagnetic environments with various interferences and noises, their performance will be limited and they will be difficult to adapt to and accurately process signal characteristics. Therefore, this article introduces the convolutional neural network algorithm based on artificial intelligence for optimization and improvement. This article focuses on exploring the methods of using CNN for signal feature extraction, and proposes the design and optimization of adaptive filters and anti-interference encoding and decoding techniques. Finally, through two sets of simulation experiments, the following conclusions were drawn: compared with traditional methods, the overall average improvement in synchronization accuracy index of CNN is 8.3%, while the signal error rate index is comprehensively reduced by 17.35%. This end-to-end learning and optimization strategy makes CNN signal recovery more effective in complex environments and improves the overall performance and reliability of communication systems.