## 1 Introduction
It is worth noting that adjacent LIS antenna elements are usually spaced at \(\lambda/2\), and the channel correlation between them enables the formation of the described beam. It is worth noting that, as defined in [8], Reconfigurable Intelligent Surfaces (RISs) are another emerging technology closely related to LISs. While LISs typically involve active antenna elements with signal processing capabilities, RISs are usually passive or semi-passive structures that reflect incident signals by controlling their phase shifts, without active transmission. Both architectures face common challenges, particularly regarding channel estimation with large-scale surfaces and hardware constraints in millimeter wave communications and terahertz environments. Recent works on RIS-assisted systems, such as [9; 10], have proposed parametric and two-stage estimation techniques to address these issues. The neural-network-based approach we propose for LISs could also inspire future research on RIS-related receiver strategies. In LISs, the use of a large number of receiving antennas allows for a better spatial resolution and increased diversity, enabling the system to distinguish and recover multiple transmitted signals more effectively. Traditional receivers such as MRC (Maximum Ratio Combining), EGC (Equal Gain Combining), Zero-Forcing (ZF), and MMSE (Minimum Mean Squared Error) have been widely studied and applied due to their mathematical tractability and relatively low complexity [7; 11; 12]. However, these methods are often limited by assumptions such as perfect channel state information and the linearity of the channel model. Moreover, solutions based on matrix inversion--such as ZF and MMSE--can become computationally expensive when scaling to a large number of antennas [7; 12]. A key challenge in LIS-based communication systems is multi-user interference (MUI), which significantly impacts system performance. Receivers, such as MRC and EGC, generate interference in the decoding process, which tends to degrade performance. Contrarily, ZF and MMSE receivers do not generate interference in the decoding process, but they require matrix inversion, leading to computational challenges. In contrast, deep learning approaches offer the possibility of learning implicit representations of the channel and estimating the transmitted symbols directly from the received signals, without requiring an explicit inversion of the channel matrix. Recent advances have demonstrated the potential of deep learning to replace traditional detection and equalization techniques in communication systems, either by training detection algorithms without any prior knowledge of the channel model [13] or by jointly learning equalization and decoding processes to improve performance in non-linear and dispersive channels [14]. This opens the door to more robust and potentially lower-complexity receiver architectures, especially when optimized for specific environments or channel conditions. In particular, deep-learning-based receivers can adapt to complex propagation scenarios, non-linearities, and hardware impairments and may be trained to outperform traditional linear techniques under certain conditions. The motivation of this work is, thus, to investigate whether a neural-network-based receiver can provide a viable alternative to conventional receivers, especially MRC and EGC, which are attractive for their simplicity, in the context of LIS systems. This includes evaluating whether a deep neural network can implicitly learn to perform the same role as these techniques (or better) while maintaining a low computational complexity and reducing the reliance on analytical channel models. In this work, we investigate an LIS system [11; 12; 15; 16; 17] employing single-carrier transmission with frequency-domain equalization (SC-FDE) [18; 19], which serves as an alternative to orthogonal frequency division multiplexing (OFDM). Since OFDM signals typically exhibit a high peak-to-average power ratio (PAPR), their use in uplink scenarios tends to be less efficient.