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Digital network twin (DNT) is a promising paradigm to replicate real-worldcellular networks toward continual assessment, proactive management, andwhat-if analysis. Existing discussions have been focusing on using only deeplearning techniques to build DNTs, which raises widespread concerns regardingtheir generalization, explainability, and transparency. In this paper, weexplore an alternative approach to augment network simulators withcontext-aware neural agents. The main challenge lies in the non-trivialsimulation-to-reality (sim-to-real) discrepancy between offline simulators andreal-world networks. To solve the challenge, we propose a new learn-to-bridgealgorithm to cost-efficiently bridge the sim-to-real discrepancy in twoalternative stages. In the first stage, we select states to query performancesin real-world networks by using newly-designed cost-aware Bayesianoptimization. In the second stage, we train the neural agent to learn the statecontext and bridge the probabilistic discrepancy based on Bayesian neuralnetworks (BNN). In addition, we build a small-scale end-to-end network testbedbased on OpenAirInterface RAN and Core with USRP B210 and a smartphone, andreplicate the network in NS-3. The evaluation results show that, our proposedsolution substantially outperforms existing methods, with more than 92\%reduction in the sim-to-real discrepancy.

Learn to Augment Network Simulators Towards Digital Network Twins