# 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 this context, the Rician K-factor, which typically ranges from 1 to 10, quantifies the ratio of the LOS power to the power of the scattered non-line-of-sight (NLOS) components and serves as a critical parameter characterizing the degree of fading and the dominance of the LOS path. To validate MIMONet's performance under different propagation environments, we conducted additional simulations for a \(4\times 4\) MIMO system with 16-QAM modulation across the Rician fading channel. The following Figure 7 presents a performance analysis of MIMONet against linear detectors MMSE and ZF for a \(4\times 4\) MIMO system with 16-QAM modulation under two different channels, including a Rayleigh fading channel and the Rician fading channel. The simulation results show that MIMONet, MMSE, and ZF with a Rician fading channel perform better than MIMONet, MMSE, and ZF with a Rayleigh fading channel because of the LOS effect. #### 6.1.4 Training and Validation of MIMONet for \(4\times 4\) and \(8\times 8\) MIMO Systems

We also analyze the training, testing, and validation of the MIMONet detector based on RMSE and loss for \(4\times 4\) and \(8\times 8\) MIMO systems. Figure 8 shows the training performance of MIMONet for a \(4\times 4\) MIMO system using 16-QAM modulation. The root mean square error (RMSE) curve exhibits a rapid decline from above 1.4 to below 0.4 over 2000 epochs, indicating effective learning and stable convergence. The training and validation loss curves follow a similar trend, demonstrating consistent reduction without signs of overfitting. The close alignment between training and validation results confirms that MIMONet generalizes well to unseen data in moderate MIMO configurations. In Figure 9, the training dynamics for an \(8\times 8\) MIMO system show a higher initial RMSE (above 2.2) due to increased complexity but follow a steady decline to below 0.5. The corresponding loss curve decreases smoothly, indicating stable optimization throughout. Despite the larger system size, the close match between training and validation metrics demonstrates MIMONet's scalability and robustness, validating its effectiveness in high-dimensional MIMO detection tasks. Figure 7: Comparative analysis of MIMONet for a \(4\times 4\) MIMO System with 16-QAM modulation under a Rayleigh fading channel and a Rician fading channel. ## 7 Computational Complexity

The computational complexity of the ZF, MMSE, and MLD MIMO detectors mainly depends on the number of antennas and constellation size; however, for AI-based MIMO detectors, it majorly depends upon network architecture [3]. In this scenario, due to the customized lightweight FFNN architecture, the MIMONet has much lower computational complexity than benchmark MIMO detectors. Figure 8: Training, testing, and validation of the MIMONet detector based on RMSE and loss for a \(4\times 4\) MIMO system. Figure 9: Training, testing, and validation of the MIMONet detector based on RMSE and loss for the \(8\times 8\) MIMO system. #### Computational Complexity of MIMONet Detector

The MIMONet model is designed using a customized FFNN architecture consisting of three hidden layers, each with 128 neurons. It employs the Clipped ReLU activation function, which offers a balance between nonlinearity and gradient stability, making it suitable for DL applications in signal detection. The computational complexity of the model is predominantly influenced by the fully connected layers. In general, the complexity of such a network can be represented by the total number of weight multiplications across all layers:

\[\mathcal{C}_{\text{MIMONet}}=\mathcal{O}\left(\sum_{l=1}^{L^{\prime}}n_{l} \cdot n_{l-1}\right)=\mathcal{O}\Big{(}128\cdot a+128^{2}+128\cdot m\Big{)} \tag{32}\]

Here, \(L^{\prime}\) denotes the number of layers \(n_{l}\), and \(n_{l-1}\) represents the number of neurons in the current and preceding layers, respectively; \(a\) is the input feature dimension, and \(m\) is the output dimension.