Anti-interference technology for wireless communication based on greedy reinforcement learning algorithm

As the most valuable area in the telecommunications industry, wireless communication has shown great potential for development in the 21st century. With the massive popularity of smartphones and 5G technology, how to create a high-quality wireless communication transmission network gradually becomes a key problem that needs to be broken through urgently at present. The study proposes a corresponding greedy reinforcement learning algorithm based on the establishment of an interference-resistant wireless communication model, which performs direct retention of high-value actions as a way to avoid extensive network computation. The results show that the algorithm achieves a fitness value of 99.1 and converges to 99.9 at about 19 iterations in the handwritten digital image set. It indicates the algorithm has a fast convergence speed in incorporating the dual network structure and empirical recovery, which can effectively enhance the learning efficiency of the anti-interference of wireless communication system and provide a new reference method for the development of anti-interference technology of wireless communication.