## 4 Simulation Results and Analysis  
This section analyzes the performance of the LIS in terms of bit error rate (BER) as a function of \(E_{b}/N_{0}\), where \(E_{b}\) denotes the energy per transmitted bit and \(N_{0}\) represents the one-sided noise power spectral density. The results are obtained via Monte Carlo simulations, using SC-FDE for block-based transmission. In the simulations, user locations are randomly distributed within the environment. The propagation channel comprises the following five multipath components: one direct line-of-sight (LoS) path and four reflections from surrounding walls. The power of each component is determined based on propagation characteristics, including the total path length and reflection-induced attenuation. The results are based on QPSK modulation and a block size of \(N=256\) symbols, with a comparable performance observed for other large values of \(N\). Figure 3 illustrates the 3D spatial distribution of users and the placement of the LIS panels, offering insight into how users align with the panels. The simulation environment consists of four ceiling-mounted panels, each equipped with a varying number of antennas. For the sake of simplicity, this figure only shows direct paths, not reflected paths. In addition to conventional iterative receivers based on MRC and EGC used to mitigate the interferences generated in the decoding process, this work explores the application of neural networks for interference cancellation during the decoding process. These networks are trained to mitigate interference effects and optimize signal recovery. The Monte Carlo simulations incorporate multiple user distributions, as the positions of users are randomly reselected in each run, ensuring statistical robustness in the analysis. The performances obtained with the different receiver types are analyzed in multiple scenarios with various numbers of users and different numbers of antennas. Figure 4 presents the performance results for an LIS setup consisting of four panels, each equipped with 400 antennas, arranged in a 20 \(\times\) 20 configuration per panel. This results in a total of 1600 antennas within the LIS. The scenario includes the following two users: one serving as the reference user and the other acting as an interfering user. As can be seen, the performance obtained with the MRC iterative receiver (five iterations) is approximately the same as that with ZF and MMSE receivers. Nevertheless, it is worth noting that ZF and MMSE receivers require the computation of the pseudo-inverse of the channel matrix for each frequency component of the channel, which is very computationally demanding. On the other hand, the EGC iterative receiver (five iterations) performs worse than MRC/ZF/MMSE. Observing the performance obtained with MRC and EGC associated with the neural network receiver, a performance improvement in the order of 5 dB, as compared with the corresponding iterative receiver, is clear. Note that the performances are even better than ZF and MMSE. It is viewed that the MRC neural receiver is the one that achieves the best overall performance, without requiring explicit channel matrix inversion. The neural network significantly minimizes the requirement for multiple matrix inversions, which are computationally demanding, enhancing the system's efficiency in both processing speed and energy usage. The training can be computationally demanding, but the prediction is straightforward. In contrast to conventional approaches, the neural network offers a more resource-efficient alternative while maintaining a high level of accuracy in signal estimation. These results demonstrate that leveraging neural networks in LIS receivers provides a promising alternative to conventional interference cancellation techniques, paving the way for efficient and scalable implementations in future 6G communication systems. Figure 3: LIS scenario considered in the simulations with four panels on the ceiling (only direct paths are plotted).