# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet: Training and Testing
#### 5.2.3 Testing
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of transmitters & 4, 8, 16 \\ \hline Number of receivers & 4, 8, 32 \\ \hline Modulation & QPSK, QAM \\ \hline Constellation size & 4, 16 \\ \hline Data size & 1 Million \\ \hline SNR Range & 0:2:20 dB and 0:5:20 dB \\ \hline Training Function & Adam \\ \hline Number of neurons & 128 \\ \hline Number of hidden layers & 3 \\ \hline Maximum epochs & 2000 \\ \hline Mini Batch Size & 10 \\ \hline Initial learning rate & 0.001 \\ \hline L2-regularization & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. ZF, MLD, GS, CG, OCDBOX, and ADMIN) and several state-of-the-art AI-driven detectors such as AIDETECT, AMP-DNN, OAMP-Net, OAMP-Net2, and DetNet. The assessment is carried out through comprehensive simulation studies across two representative MIMO configurations, 4 \(\times\) 4 and 8 \(\times\) 8 systems, each featuring an equal number of transmit and receive antennas. These scenarios are designed to test the robustness, accuracy, and scalability of MIMONet under varying system complexities. #### 6.1.1 Performance Analysis of MIMONet Detector Against Traditional MIMO Detectors

To measure the performance of the MIMONet detector against traditional MIMO detectors, we conducted simulations for 4 \(\times\) 4 and 8 \(\times\) 8 MIMO scenarios. Figure 2 presents the BER performance of various signal detection algorithms evaluated within a 4 \(\times\) 4 MIMO system using QAM modulation as a function of the average SNR per receive antenna. The DL-based detector, MIMONet, exhibits clear superiority across the full SNR range. At an SNR of 20 dB, MIMONet achieves an exceptionally low BER, outperforming conventional detectors by two to three orders of magnitude. This significant performance margin highlights the capacity of DNNs to learn complex signal features and optimize detection boundaries that are difficult to model analytically. In contrast, the MLD algorithm shows a distinct performance trajectory underperforming at low SNRs but demonstrating rapid improvement beyond 15 dB, eventually ranking as the second-best performer in high-SNR conditions. This behavior is consistent with the theoretical nature of MLD, which becomes increasingly effective as the noise level diminishes. Among conventional linear and iterative detectors, the GS method consistently achieves marginally better BER performance compared to others, while ZF lags slightly at higher SNR values. MMSE and ADMIN follow similar performance patterns, with MMSE offering a slight edge at elevated SNR levels. Apart from a small-scale 4 \(\times\) 4 MIMO system, we also considered a mid-range 8 \(\times\) 8 MIMO system to analyze the performance of the MIMONet detec

Figure 2: BER performance analysis of the MIMONet detector against benchmark traditional MIMO detectors for a 4 \(\times\) 4 MIMO system. detectors. Figure 3 presents the BER performance of various detection algorithms within an \(8\times 8\) MIMO system using QAM modulation. Compared to the \(4\times 4\) configuration, this higher-dimensional setup introduces increased detection complexity and greater signal interference. Despite these challenges, the DL-based MIMONet consistently demonstrates the lowest BER across all SNR values, showcasing its remarkable ability to generalize and scale in larger antenna systems. The MLD algorithm continues to exhibit strong performance in the high-SNR regime, ranking as the second-best performer. However, its BER curve lies noticeably above that of MIMONet, illustrating the exponential performance trade-off and computational limitations associated with exhaustive search strategies in larger MIMO configurations. In contrast, conventional detection techniques including MMSE, ZF, CG, GS, OCDBOX, and ADMIN exhibit closely grouped, relatively high BER curves. Their limited improvement at increasing SNR levels indicates difficulty in effectively mitigating inter-stream interference in the expanded signal space of the \(8\times 8\) system.