# 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
In addition to the \(4\times 4\) and \(8\times 8\) MIMO scenarios, we extended our simulation study to a \(16\times 32\) ma-MIMO system. Figure 4 presents the BER performance of various detection algorithms within a \(16\times 32\) ma-MIMO system using QAM modulation. Despite the higher-dimensional setup, the simulation results show that the proposed MIMONet model performs better than the traditional benchmark MIMO detectors at all SNR points. #### 6.1.2 Performance Analysis of MIMONet Detector Against AI-Based MIMO Detectors

To compare the performance of the MIMONet detector against AI-based MIMO detectors, we conducted simulation experiments based on QPSK and 16-QAM modulation schemes for \(8\times 8\) MIMO scenarios. Figure 5 presents the BER performance of several DL-based detection algorithms for an \(8\times 8\) MIMO system using QPSK modulation. The simulation results show that, among all AI-based MIMO detection methods, MIMONet consistently demonstrates superior detection accuracy across the entire SNR range. At

Figure 3: BER performance analysis of the MIMONet detector against benchmark traditional MIMO detectors for the \(8\times 8\) MIMO system. a 20 dB SNR, MIMONet achieves a BER nearing (\(1\times 10^{-6}\)), significantly outperforming all competing models by several orders of magnitude. AIDETECT emerges as the second-best performer; however, its performance remains substantially inferior to that of MIMONet. The AMP-DNN and OAMP-Net2 algorithms exhibit moderate detection capability, while DetNet and OAMP-Net record the highest BER values, indicating limited robustness in lower-SNR regimes and constrained generalization under QPSK conditions. This analysis underscores MIMONet's exceptional learning capacity and resilience, even when trained with relatively modest computational resources and low-order modulation schemes. Moreover, Figure 6 extends the performance evaluation to a more complex modulation format--16-QAM--within the same \(8\times 8\) MIMO system. Despite the increased modulation complexity, MIMONet once again outperforms all other algorithms, achieving a BER below (\(3\times 10^{-5}\)) at 20 dB SNR. The detection margin between MIMONet and the next-best algorithm, AIDETECT, becomes even more pronounced compared to the QPSK scenario, highlighting MIMONet's scalability and its proficiency in learning sophisticated signal patterns. Although AMP-DNN improves in higher SNR regions, it remains unable to match MIMONet's performance. Meanwhile, DetNet, OAMP-Net, and OAMP-Net2 retain their relative positions as lower-performing detectors, particularly struggling to suppress symbol errors effectively in dense signal constellations. These findings confirm that MIMONet's advanced neural architecture and extensive training regimen are highly effective in managing the added challenges posed by higher-order modulation in large-scale MIMO systems. Figure 4: BER performance analysis of the MIMONet detector against benchmark traditional MIMO detectors for the 16 \(\times\) 32 ma-MIMO system. #### 6.1.3 MIMONet Under Diverse Channel Conditions

Our study primarily focused on the Rayleigh fading channel. However, to ensure a more comprehensive evaluation across different channel conditions, we incorporated the

Figure 5: BER performance analysis of the MIMONet detector against benchmark AI-based MIMO detectors for an \(8\times 8\) MIMO system with a QPSK modulation scheme. Figure 6: BER performance analysis of the MIMONet detector against benchmark AI-based MIMO detectors for an \(8\times 8\) MIMO system with a 16-QAM modulation scheme. Rician fading channel to represent the distinct wireless propagation conditions. The Rician fading channel represents a practical scenario where the line of sight (LOS) components exist, making the evaluation more representative of diverse communication conditions.