Autonomous Link Control in Digital-Twin-Aided Mobile Network: From Virtual Channel Generation to Intelligent Power Allocation

In the mobile network, digital twin (DT)-aided artificial intelligence (AI)-empowered link control is vital to enhance the performance of wireless communication. This article proposes a deep reinforcement learning (DRL)-convex optimization enhanced time-frequency domain power allocation scheme to reduce the long-term average bit-error-rate (BER) in multiuser orthogonal frequency division multiplexing (OFDM) systems. To alleviate performance loss caused by trial-and-error during the training period of DRL algorithms, we design a novel practical DT-aided 'prediction-then-decision' autonomous wireless link control framework considering the periodic interaction mechanism between the DT and its physical counterpart. A Transformer-based channel generator Mucomformer is implemented in the DT layer to generate large amounts of multiuser virtual channel state information (CSI) in future transmission frames. In addition, the DRL agent is trained over the DT channel in advance and executed in the real-world OFDM system to generate the optimal transmission strategy by considering the interaction mechanism between the DT and the physical counterpart. The simulation results demonstrate that the proposed Mucomformer has lower average prediction error of 2.51 dB compared to the Transformer baseline. The DRL and convex-based power allocation scheme further outperforms the classic strategy. Moreover, the practical DT-aided autonomous link control framework effectively mitigates the performance impairment, achieves an average BER performance gain 45.65% higher than that without DT and achieves faster convergence during the whole training period.