Photonic Beamforming for mmWave MIMO via Machine Learning Inverse Design

Abstract

Fifth- and sixth-generation (5G/6G) mobile networks demand advanced beamforming for millimeter-wave (mmWave) massive MIMO systems. Conventional electronic beamforming suffers from high complexity, power consumption, and latency. This paper presents an inverse design framework using machine learning (ML) for photonic beamforming networks, enabling intelligent control of variable optical delay lines (VDLs) integrated with array antennas. The method optimizes beam directivity for single-user and enables multi-user (MU-MIMO) beamforming. Furthermore, deep neural networks (DNN), tabular data neural network (TabNet), traditional neural networks (NN), long short-term memory networks (LSTM), extreme gradient boosting (XGBoost), and lightweight gradient boosting machine (LightGBM) are involved in the proposed inverse design ML models for thorough comparison. Results show LightGBM achieves the highest accuracy for VDL prediction due to its tabular data processing efficiency. This work provides a foundation for low-complexity, low-power photonic front-ends for 5G/6G.