Optimization of Interference Identification for Wireless Communication Based on Global Convolutional Neural Network

With the widespread adoption of smartphones and the rise of 5G technology, wireless networks fully show their bright future. In order to study the interference factors and optimization methods in the process of wireless communication, this paper studies the wireless communication interference recognition algorithm and power allocation algorithm based on global convolutional neural network, proposes a wireless communication interference recognition system model based on global convolutional neural network, and trains the system. The specific conclusions are as follows: (1) The basic modulation recognition neural network algorithm is constructed based on deep learning theory; (2) As the number of D2D users increases, the time used by the WMMSE algorithm and the equal power allocation algorithm in the traditional method increases. (3) The QL algorithm-based on greedy- is compared with the system-based QL algorithm proposed in this paper. On 200-time nodes, the SINK value of the QL algorithm based on -greedy is increased from 2.6 to 3.3, and the convergence speed is also greatly improved. The system proposed in this paper has a faster learning speed, can accelerate the learning of strategies, and can save a lot of computing resources, and effectively reduce the complexity of operations.