Integration of Deep Learning Methods into the Design of Microwave Transceiver Components for 5G Mid-Band System

This study evaluates the application of deep learning methods to the design of a microwave transmitter–receiver system operating in the mid-band of 5G communications. The proposed system comprises four stages—signal generation, amplification, mixing, and filtering—each designed individually using traditional microwave theory and then integrated into a full transceiver. Simulation data were generated in MATLAB and ADS, and four convolutional neural networks (CNNs) were implemented in Python (TensorFlow/Keras), with architectures ranging from 11 to 271 layers and training datasets between 4,000 and 12,000 samples. Training was performed over 200–1,000 epochs using Adam optimization, ReLU/linear activations, and sequential dense connections. Across all networks, the average error reduction exceeded 90%, with convergence achieved after the third training cycle for most components. For the transceiver integration, baseline design simulations indicated a transmitted power of –32.637 dBm with a gain of 1.116 dB. The deep learning-based design yielded comparable results, with a transmitted power of –33.912 dBm and a gain of 0.738 dB. These results demonstrate that the neural network models successfully approximated the behavior of microwave components without degrading system-level performance. Further analysis of scattering parameters (S-parameters) confirmed that the CNN-trained models maintained acceptable matching and frequency response across the 3.5 GHz operating band. Overall, this study demonstrates a complementary design methodology for microwave systems in 5G applications, enabling the modeling and optimization of multiple components simultaneously.