High spectral efficiency, large capacity, and high data rate make large-scale Multiple-Input Multiple-Output (MIMO) systems more prominent in 5G communication. Existing MIMO detection schemes like Maximum Likelihood (ML) achieves optimal performance, but it is significantly affected by exponentially increasing computational complexity. On the other hand, linear MIMO detection techniques have low complexity. However, these cannot be implemented in practical systems due to poor performance. Ordered Successive Interference Cancellation (OSIC) addresses these issues as it possesses intermediate and suboptimal performance but it faces erroneous symbols due to wrong selection of order. At first, OSIC K-correction algorithm is employed to resolve OSIC issues by replacing the user-defined K symbols in the search subspace of OSIC symbol vector and detects the symbol in terms of better likelihood metric. In addition, it proposes Reduced Complexity (RC) OSIC K-correction scheme that further reduces the computational complexity in OSIC K-correction by exploiting the deviation vector which focuses to opt and detect only the most erroneous symbols. The proposed algorithms offer an adequate trade-off between computational complexity and detection performance.

高频谱效率，大容量和高数据速率使大规模多输入多输出（MIMO）系统在5G通信中更加突出。现有的MIMO检测方案（例如最大似然（ML））可实现最佳性能，但受到计算复杂度呈指数增长的显着影响。另一方面，线性MIMO检测技术具有低复杂度。然而，由于性能差，这些不能在实际系统中实现。有序连续干扰消除（OSIC）解决了这些问题，因为它具有中等和次优的性能，但是由于错误的顺序选择而面临着错误的符号。首先，OSIC K校正算法用于通过替换OSIC符号向量的搜索子空间中的用户定义的K符号来解决OSIC问题，并根据更好的似然度量来检测符号。此外，它提出了降低复杂度（RC）的OSIC K校正方案，该方案通过利用偏差矢量进一步降低OSIC K校正的计算复杂度，该偏差矢量专注于仅选择和检测最错误的符号。所提出的算法在计算复杂度和检测性能之间提供了适当的权衡。