## 5 Conclusions  
This work introduces a neural-network-based approach for interference cancellation in LIS systems employing MRC and EGC receivers. The proposed method replaces traditional iterative interference cancellation techniques by leveraging an MLP to estimate  
Figure 8: Performance results of the 4 \(\times\) 400 LIS, with several numbers of users, and with different receiver types.  
Figure 7: Performance results of the 4 \(\times\) 400 LIS, with 16 users, with different receiver types.  
the transmitted signals directly from the output of a single-iteration MRC/EGC receiver, which comprises interference-corrupted signals. This approach effectively mitigates the interference generated in the decoding process of MRC/EGC, improving signal recovery without requiring explicit channel matrix inversion, as required for ZF and MMSE receivers. Moreover, it is observed that the MRC/EGC neural network outperforms MMSE and ZF. Finally, it is observed that the MRC always outperforms EGC (with or without a neural network).  
The chosen network architecture, consisting of three fully connected layers with ReLU activation, is selected based on extensive empirical evaluations, balancing complexity and performance. A deeper network does not yield significant improvements, while a shallower one results in a degraded performance. The simulation results demonstrate that the proposed neural-network-based approach outperforms traditional iterative interference cancellation methods in terms of signal reconstruction accuracy while maintaining computational efficiency. The decision to train separate neural networks per transmit antenna is justified by the statistical independence of antennas, ensuring robustness against varying channel conditions. Additionally, alternative architectures such as CNNs and LSTM networks are considered, but are deemed less suitable due to the absence of strong spatial or temporal dependencies in the interference structure.  
Overall, this work confirms that neural networks provide a promising alternative for interference mitigation in LIS receivers, offering a practical and scalable solution for future 6G communication systems.  
Future work could explore deeper neural architectures and test the proposed approach in real-time scenarios to validate its practical applicability. Moreover, although this work focuses on comparing neural-network-based receivers with conventional MRC, EGC, ZF, and MMSE techniques, future work will also consider iterative ZF/MMSE approximations that avoid matrix inversion, such as those based on the [PERSON], Newton, or Neumann methods, as part of a broader comparative study.  
Research and numerical analysis was carried out by the first author, whereas the other authors supported conceptualization. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MECI through national funds and when applicable co-funded EU funds under UID/50008: Instituto de Telecomunicacoes.  
Monte Carlo simulations were performed using MATLAB R2025a to obtain the data presented.  
The authors acknowledge the support of FCT/MCTES (as detailed in the Funding Section) and Autonoma TechLab for providing a stimulating research environment.  
The authors declare no conflicts of interest.