# 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
This formulation captures the cumulative computational load during a single forward pass through the network. Table 2 summarizes the computational complexity of different MIMO detectors. In addition to the table comparison, we conducted a complexity comparison of MIMONet against the benchmark MIMO detectors, as shown in Figure 2:0 This complexity comparison is carried out in terms of the number of FLOPs, which provides a fair evaluation of the computational cost related to each detection approach. \begin{table}
\begin{tabular}{c c} \hline \hline
**MIMO Detectors** & **Computational Complexity** \\ \hline \hline \multicolumn{1}{c}{MLD [2]} & \(\mathcal{O}(Q^{P_{t}})\)\(Q^{\prime}=\)_modulationorder_ \\ \hline \hline \multicolumn{1}{c}{MMSE [12]} & \(\mathcal{O}(P_{t}^{3})+\mathcal{O}(Q_{u}P_{t}^{2})+\mathcal{O}(P_{t}Q_{u}^{2})\) \\ \hline \hline \multicolumn{1}{c}{ZF [12]} & \(\mathcal{O}(P_{t}^{3})+\mathcal{O}(Q_{u}P_{t}^{2})\) \\ \hline \hline \multicolumn{1}{c}{CG [34]} & \(\mathcal{O}((T+1)(4P_{t}^{2}+20P_{t}))\)\(T=iterations\) \\ \hline \hline \multicolumn{1}{c}{NS [35]} & \(\mathcal{O}((T-1)(2P_{t}^{3}+2P_{t}^{2}-2P_{t}))\) \\ \hline \hline \multicolumn{1}{c}{GS [36]} & \(\mathcal{O}(6 TP_{t}^{2})\) \\ \hline \hline \multicolumn{1}{c}{OCDBOX [37]} & \(\mathcal{O}(T(2Q_{u}P_{t}+P_{t}))\) \\ \hline \hline \multicolumn{1}{c}{ADMIN [37]} & \(\mathcal{O}(P_{t}^{3}+Q_{u}^{2}+Q_{u}+T(P_{t}^{2}+P_{t}))\) \\ \hline \hline \multicolumn{1}{c}{OAMP-Net [38]} & \(\mathcal{O}(P_{t}^{3})\) \\ \hline \hline \multicolumn{1}{c}{OAMP-Net2 [39]} & \(\mathcal{O}(L^{\prime}Q_{u}^{3})\) (\(L^{\prime}\) = number of layers) \\ \hline \hline \multicolumn{1}{c}{DetNet2 [10]} & \(\mathcal{O}(L^{\prime}Q_{u}P_{t}^{2})\) \\ \hline \hline \multicolumn{1}{c}{AMP-DNN [40]} & \(\mathcal{O}(L^{\prime}Q_{u}P_{t})\) \\ \hline \hline \multicolumn{1}{c}{AIDETECT [11]} & \(\mathcal{O}(\sum_{n=3}^{N}N_{n-1}N_{n}\) (\(N_{n}\) = number of neurons in nth layer) \\ \hline \hline \multicolumn{1}{c}{MIMONet} & \(\mathcal{O}\left(\sum_{l=1}^{L^{\prime}}n_{l}\cdot n_{l-1}\right)=\mathcal{O} \big{(}128\cdot a+128^{2}+128\cdot m\big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The computational complexity of MIMO detectors based on Big O notation. ## 8 Conclusions and Future Work

AI has significantly advanced the capabilities of MIMO systems, positioning them as key enablers for B5G and 6G networks. However, reliable signal detection remains a core challenge due to the increasing complexity of MIMO environments. This work proposes MIMONet, a lightweight DL-based detector leveraging a customized FFNN architecture for efficient MIMO signal detection. The proposed MIMONet demonstrates strong performance across both \(4\times 4\) and \(8\times 8\) MIMO configurations, achieving benchmark performance while maintaining low computational complexity compared to conventional model-based and learning-based detectors. These results validate the potential of data-driven architectures for efficient and scalable signal detection in modern wireless systems. To expand MIMONet's applicability to realistic 6G situations, further research will investigate its scalability to ultra-massive MIMO systems (such as \(64\times 64\) and \(128\times 128\)), multi-user MIMO, and frequency-selective channels. Additionally, to improve generalization, we will use sophisticated training techniques, including transfer learning and data augmentation, and examine MIMONet's resilience to non-Gaussian noise models. Future work will also focus on federated learning for distributed MIMO, noncoherent detection, and a thorough examination of hardware-level performance and training complexity, all of which will be backed by COST2100 or the NYU mm-Wave channel models. [PERSON] was responsible for software development, preparing the original draft, and making subsequent revisions. [PERSON] contributed significantly by writing and critically reviewing the literature section of the manuscript, ensuring comprehensive coverage and clarity.