Intelligent Massive MIMO Antenna Weight Optimization Using C-GAN Aided Digital Twin

The antenna weight optimization plays a vital role in improving the key performance indicators (KPIs) of massive multi-input multi-output (MIMO) systems. Due to complicated channel characteristics and numerous parameter combinations, conventional methods suffer from suboptimal performance and unaffordable complexity. Additionally, the implementation of existing intelligent algorithms in the practical system is limited by heavy overhead and potential instability associated with environmental interactions. In this paper, we propose a digital twin assisted optimization framework comprising a conditional generative adversarial network (C-GAN) aided digital twin and a deep reinforcement learning (DRL) based massive MIMO antenna weight optimization algorithm to maximize the KPI of coverage. The C-GAN aided digital twin is built to augment system performance data and offer high-precision KPI pre-validation by fitting the mapping between beamforming schemes and system performance along with the distribution of system performance over user positions. The DRL based optimization algorithm that achieves a better complexity-performance tradeoff is combined with digital twin for reduced training overhead and safe exploration. Simulation results based on quasi deterministic radio channel generator (QuaDRiGa) verify that compared to intelligent optimization methods directly conducted in practical systems, our proposed framework can reduce the overhead while achieving comparable performance by leveraging high-precision pre-validation capabilities of the proposed digital twin.