# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 5 Conclusions  
This paper proposed an FDNN-based approach for signal detection in MIMO-NOMA scenarios. This algorithm addresses the high BER issue in SIC receivers caused by the  
\begin{table}
\begin{tabular}{c c c} \hline
**User** & \(\Delta_{SNR}=3\)**dB** & \(\Delta_{SNR}=7\)**dB** & \(\Delta_{SNR}=9\)**dB** \\ \hline User 1 for SIC & \(8.03\times 10^{-2}\) & \(2.55\times 10^{-3}\) & \(3.37\times 10^{-4}\) \\ \hline User 1 for FDNN & \(3.35\times 10^{-2}\) & \(1.8\times 10^{-3}\) & \(2.6\times 10^{-4}\) \\ \hline User 2 for SIC & \(1.3\times 10^{-1}\) & \(1.15\times 10^{-2}\) & \(2.45\times 10^{-2}\) \\ \hline User 2 for FDNN & \(6.22\times 10^{-2}\) & \(9.52\times 10^{-3}\) & \(2.46\times 10^{-2}\) \\ \hline \end{tabular}
\end{table}
Table 3: BER at different \(\Delta_{SNR}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**User** & \(\Delta_{SNR}=3\)**dB** & \(\Delta_{SNR}=5\)**dB** \\ \hline User 1 for SIC & \(5.95\times 10^{-2}\) & \(1.7\times 10^{-3}\) \\ User 1 for FDNN & \(1.45\times 10^{-2}\) & \(1.2\times 10^{-3}\) \\ User 2 for SIC & \(9.8\times 10^{-2}\) & \(1.75\times 10^{-5}\) \\ User 2 for FDNN & \(2.4\times 10^{-2}\) & \(1.4\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: BER at \(\text{SNR}=20\) dB.  
Figure 9: Performance of FDNN and SIC receivers under different \(\Delta_{SNR}\).  
near-far effect and error propagation. By leveraging the advantages of DNN in nonlinear detection and the low computational complexity of SIC, this paper proposes an FDNN model to replace the traditional SIC receiver, providing its algorithm flow. Simulation results demonstrate the algorithm's computational efficiency and effectiveness.  
Based on simulation results, this paper chooses 100 as the training batch size, and the neural network has a three-layer structure consisting of an input layer, two hidden layers with node counts of 16 and 32, and an output layer. This configuration enables the FDNN model to achieve better performance. This paper assesses the FDNN algorithm's effectiveness and examines the influence of the power allocation scheme on system performance. Simulation outcomes consistently demonstrate that FDNN receivers exhibit lower BER compared to SIC receivers, affirming the performance advantages of FDNN in MIMO-NOMA scenarios. In future research, the performance gap of FDNN under different conditions and scenarios will be further investigated, aiming to enhance the algorithm's stability in engineering applications.  
Conceptualization, Q.W. and T.Z.; methodology, Q.W.; software, H.Z.; validation, H.H.; writing--original draft preparation, Q.W. and E.P.d.F.; writing--review and editing, T.Z.; supervision, S.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Commission Foundation of Shanghai (Nos. 22511100600 and 23 xtcx00100), in part by the Shanghai Industrial Collaborative Innovation Project (No. XTCX-KJ-2023-05), and in part by the Pudong Industry, Education and Research Cooperation Program (No. PKX2023-D05).  
Derived data supporting the findings of this study are available from the corresponding author on request.  
The authors declare no conflicts of interest.