This paper proposes to use candidate symbol table for the decoding of the first few substreams for spatially multiplexed system. In the new decoding algorithm, we generate candidate symbol table (CST) and do ordered successive interference cancellation (OSIC) for each candidates. At the end, Maximum Likelihood (ML) criteria is used to determine the optimum transmit symbol vector among the estimated candidates. Simulation result shows that the size of CST can be maintained to be small, even if the number of bits per symbol increases. Simulation results show that this algorithm gives us performance close to ML decoder, and outperforms maximum likelihood decision feedback equalizer (ML-DFE) and channel-based adaptive group detection based OSIC (AGD-OSIC).

As smart factories are emerging, the importance of modeling and simulation (M&S) continues to increase in the production system. As a result, various commercial tools and environments are provided for production simulation, and manufacturing companies are also applying them to establish a smart manufacturing system. This is used in various ways, such as optimal layout design, scheduling, and fault diagnosis using the acquired smart manufacturing model. However, these model constructions are generally done through a stand-alone environment in which the work type or user level is not considered. It is necessary to use different environments depending on the user level or to rely on M&S experts. Therefore, this paper eliminates this inefficiency and proposes an integrative user-level customized smart manufacturing M&S environment for all users in the production system. It provides three-phase modeling environments appropriate for the user level, including an automatic model synthesis interface and a production line generator. Using this environment, anyone can easily make capacity and logistic models, and simulate them.INDEX TERMS Modeling and simulation, smart manufacturing, digital twin, user-level customized, discrete event system.

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With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site.

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