###### Abstract
Furthermore, in dynamic channel conditions, these conventional methods may lack the adaptability needed to maintain optimal performance. Neural networks (NNs) have gradually shown great potential in the field of communication, particularly in receiver design. In various systems, NNs have been successfully applied to different reception techniques to improve signal processing efficiency and accuracy. To address complex signal interference issues, many studies have begun to explore NN-based interference cancellation methods [20]. For example, [PERSON]. [21] proposed a convolutional neural network (CNN)-based method for in-band interference (IBI) cancellation, which demonstrated a significant improvement in spectral efficiency (SE) in a 100 Gbit/s/\(\lambda\) system. Compared to traditional ICA methods, this approach reduced computational complexity and improved practical feasibility. [PERSON]. [22] employed deep neural networks (DNNs) to address interference in non-orthogonal signal waveforms, significantly improving signal-to-noise ratio (SNR) and achieving performance improvements over traditional hard-decision detectors. In the underwater communication domain, [PERSON]. [23] used deep neural networks in the design of an orthogonal frequency division multiplexing (OFDM) receiver, simplifying receiver complexity and improving bit error rate (BER) and transmission efficiency over traditional methods. This demonstrates that deep learning can not only effectively address interference under different waveforms but also enhance system performance in complex environments. As system scale increases and multi-user environments are introduced, significant progress has been made in the application of deep learning for multi-user detection. For example, Ref. [24] proposed an end-to-end (E2E) framework, which adjusted the training method of the deep neural network and successfully enabled flexible communication in both single-user and multi-user scenarios. The experimental results showed that the method achieved 3.5 dB and 1.5 dB sensitivity gains in single-user and multi-user scenarios, respectively, highlighting the potential of deep learning in multi-user systems. In [25], the proposed E2E learning-based autoencoder framework was experimentally verified to effectively compensate for impairments introduced by photonic devices, such as Mach-Zehnder modulators (MZMs) and photodetectors. It demonstrated that the neural network-based approach can mitigate both linear and nonlinear impairments in mmWave communication systems. Table 1 compares existing NN-based receiving algorithms in terms of their application scenarios, main advantages, and limitations. However, while promising, many of these studies have focused primarily on single-input single-output (SISO) systems, leaving room for further research on more generalizable and robust NN-based receivers suitable for a wider range of real-world scenarios, especially in the coordinated multiple-input single-output (MISO) scenario as shown in Figure 1.  
The use of non-orthogonal multiband techniques, such as NM-CAP modulation, is particularly advantageous in multi-user systems due to their ability to significantly improve spectral efficiency. In traditional orthogonal systems, sub-bands are strictly separated by guard bands, leading to the underutilization of the available spectrum. In contrast, non-orthogonal multiband techniques allow for tighter packing of sub-bands, enabling more efficient use of the spectrum and supporting higher data rates and more users within the same bandwidth. This is especially critical in the coordinated MISO scenario depicted in Figure 1. However, the increased spectral efficiency comes at the cost of higher inter-band interference (IBI) and inter-symbol interference (ISI), which must be effectively mitigated to maintain reliable communication. Traditional DSP techniques, such as LMS equalization, often struggle to effectively mitigate ISI and IBI. These conventional