## 5 Conclusions  
In this work, we study the joint antenna selection and user allocation problem for multi-user large-scale MIMO systems. In massive MIMO systems, antenna selection and user scheduling are complementary techniques. Antenna selection reduces hardware complexity and energy consumption, while user scheduling enhances system capacity and fairness. Together, they form the core mechanism for system performance optimization, serving as key enablers for the successful deployment of massive MIMO.  
In this paper, a low-complexity joint antenna selection and user scheduling scheme is designed for a multi-user system model, which achieves a considerable system sum rate. On this basis, to balance system performance and user service fairness, an improved algorithm based on PF scheduling is proposed. This method ensures fair participation of users in communication with only a slight loss in transmission rate, making it practically meaningful.  
The integration of antenna selection and user scheduling serves as a key framework for resource allocation algorithms in wireless communication systems. It enhances the system's resilience to hardware failures and non-ideal channel conditions, offering a low-complexity, suboptimal alternative to fully connected architectures. This approach is particularly suitable for power-sensitive scenarios such as edge devices, UAVs, and satellite communications, as well as a wide range of multi-user access systems including 5G/6G, IoT, and Wi-Fi networks. Therefore, joint antenna selection and user scheduling holds profound theoretical research value and promising practical significance for future deployments.  
\begin{table}
\begin{tabular}{c c} \hline
**Software and Hardware** & **Configuration Parameters** \\ \hline Operating System & Windows 10 Professional \\ \hline Simulation Software & R2024b \\ \hline CPU & Intel(R) Core(TM) i9–9880H \\ \hline RAM & 16 GB \\ \hline GPU & Radeon Pro 560X \\ \hline VRAM & 4096 MB \\ \hline \end{tabular}
\end{table}
Table 4: Simulation software and hardware configuration.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithms** & **Simulation Time** \\ \hline JAS-OUS & 0.1640 \\ \hline JAS-CPUS & 0.1029 \\ \hline JAS-SUS & 0.2087 \\ \hline JAS-PFUS & 1.4069 \\ \hline Exhaustive Search & 1808.4610 \\ \hline \end{tabular}
\end{table}
Table 5: Simulation time for one channel realization.  
**Author Contributions:** Conceptualization, M.W.; methodology, B.C.; software, B.C. and M.W.; validation, L.Y. and M.W.; formal analysis, L.Y.; investigation, B.C.; resources, L.Y.; data curation, L.Y.; writing--original draft preparation, B.C., L.Y. and M.W.; writing--review and editing, B.C., L.Y. and M.W.; visualization, B.C. and M.W.; supervision, M.W.; project administration, M.W.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation, grant number 62372244, 62172235; the National Key Research and Development Program of China, grant number 2021 YFB3101100; the Primary Research & Development Plan of Jiangsu Province, grant number BE2023025; the Natural Science Foundation of Nanjing University of Posts and Telecommunications, grant number NY222132; the ZTE Industry-university Research Fund, grant number 2023 ZTE08-02; the Postgraduate Research & Practice Innovation Program of Jiangsu Province, grant number 46004 CX17781, SJCX220285.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.