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Realizing the potential gains of large-scale MIMO systems requires theaccurate estimation of their channels or the fine adjustment of their narrowbeams. This, however, is typically associated with high channelacquisition/beam sweeping overhead that scales with the number of antennas.Machine and deep learning represent promising approaches to overcome thesechallenges thanks to their powerful ability to learn from prior observationsand side information. Training machine and deep learning models, however,requires large-scale datasets that are expensive to collect in deployedsystems. To address this challenge, we propose a novel direction that utilizesdigital replicas of the physical world to reduce or even eliminate the MIMOchannel acquisition overhead. In the proposed digital twin aided communication,3D models that approximate the real-world communication environment areconstructed and accurate ray-tracing is utilized to simulate the site-specificchannels. These channels can then be used to aid various communication tasks.Further, we propose to use machine learning to approximate the digital replicasand reduce the ray tracing computational cost. To evaluate the proposed digitaltwin based approach, we conduct a case study focusing on the position-aidedbeam prediction task. The results show that a learning model trained solelywith the data generated by the digital replica can achieve relatively goodperformance on the real-world data. Moreover, a small number of real-world datapoints can quickly achieve near-optimal performance, overcoming the modelingmismatches between the physical and digital worlds and significantly reducingthe data acquisition overhead.

Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?