Accurate evaluation of radio access performance is essential for the design and optimization of ultra-dense fifth-generation (5G) networks, where the interactions between multiple users, base stations, and interference sources become highly complex. This paper proposes a comprehensive  System-Level Simulation (SLS) framework for assessing the performance of multi-user multiple-input multiple-output (MIMO) systems in indoor hotspot environments, following the standardized 3rd  generation partnership project (3GPP) InH_B channel model. The proposed framework integrates realistic network-level processes, including user equipment deployment, three-dimensional channel coefficient  generation, analog and digital beamforming, link adaptation, and block error rate prediction. Different  from conventional SLS studies that often decouple receiver processing, channel state information (CSI)  uncertainty, and link abstraction, the proposed framework provides a unified receiver-aware evaluation chain calibrated against 3GPP Phase 2 results. Both Maximal Ratio Combining (MRC) and Interference  Rejection Combining (IRC) receivers are implemented to investigate the impact of imperfect CSI on system performance. Extensive simulations demonstrate that the proposed framework provides close agreement with the 3GPP Phase 2 calibration results, while enabling detailed receiver-level comparisons under practical CSI conditions. When CSI errors are considered, the IRC receiver achieves approximately  42% higher average cell throughput and more than 160% improvement in 5% user throughput compared to MRC, confirming its superior robustness to interference and channel estimation errors. The developed framework serves as a practical reference platform for future research on intelligent beamforming,  adaptive MIMO receiver design, and machine learning-based link adaptation for beyond-5G and sixth-generation (6G) systems.