Deep Learning for Antenna Parameter Optimization in Wireless Communications

Communication technology in recent days has undergone rapid growth and the need to improve the performance of antennas has also got its equal importance. The inclusion of the optimization methods satisfies the need to increase performance. In this paper, the inset-fed rectangular patch antenna is optimized using Deep learning along with Particle Swarm Optimization (PSO). The neural network is trained using the datasets and PSO is adapted to optimize the antenna parameters. The output from the neural network is the relationship between the model parameters and the antenna parameters. The antenna designed from the neural networks output is found to have best performance in terms of directivity, gain, efficiency, and miniaturization. The simulated results show that the antenna has a 24% reduction in size and a 70% improvement in efficiency and is used for applications in the 5.8 GHz (ISM band).