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The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments. [ABSTRACT FROM AUTHOR]