### Related Work and Motivation  
Each user in the SCMA system is assigned a unique known codebook, in which the information bits are multiplied by the mapping matrix and mapped into multi-dimensional codewords [2]. Therefore, the design of the codebook is a crucial factor that directly influences the performance of SCMA systems. The authors of [3] introduced the first codebook for SCMA, which laid the theoretical foundation for subsequent SCMA codebook designs. Improvements were made to the codebook design in [4], and a temporary codebook that can be automatically updated with reverse derivation was developed in [5]. Ref. [6] proposed a method named simplified decomposition of the superposed constellation (S-DCSC), which gained lager superposed symbols in the receiver than other codebook design schemes. The codebook design techniques are continually evolving to facilitate a more effective SCMA system. The receiver design also plays an essential role in SCMA performance. The same time-frequency resources can be shared by multiple users in an SCMA system. As a result, it requires a more complex receiver design than an OMA-based system. The traditional decoding method for SCMA relies on the message passing algorithm (MPA) [7], which has a high computational complexity. Ref. [8] removed the exponential operations in the message updates in order to lower the computational complexity; however, this had a major negative impact on the bit error rate (BER) performance. Ref. [9] proposed a shuffled MPA (SMPA) that employs a different message updating approach, reducing the computational complexity and concurrently improving the BER performance. Based on the works of [9], a low-complexity MPA algorithm (TB-RMPA) was proposed in [10]. TB-RMPA further decreased the computational complexity, but showed a minor decline in terms of the BER performance. Although these MPA-based derivative algorithms have improved the strategy of message iteration, they still require a considerable number of cyclic iterations, which struggle to meet the low latency requirements of the decoder. Deep learning (DL) technology is a revolutionary technique with versatile applications across various fields. Although the process of DL-based model training can be time-consuming, the achieved high efficiency and low complexity after training, along with its strong recognition and classification capabilities, make it highly suitable for signal detection systems [11]. It has been shown that the processing of complex received-signal classification in SCMA systems is compatible with powerful deep neural networks (DNNs) [12], leading to the growing popularity of DL-based SCMA decoding techniques. Compared with the approach in [13], which integrated MPA logic into a DNN, both [14; 15] forevent MPA logic altogether in the decoder and instead focused on optimizing the DNN structure and selecting appropriate functions. Building upon the work of [15], Ref. [16] explored incorporating prior knowledge migration before decoding, resulting in enhanced performance. However, the aforementioned techniques may not fully utilize the potential of DNNs' capability for feature extraction in data categorization tasks. In this paper, our main emphasis is on enhancing the performance of SCMA decoders. For a downlink SCMA system with \(J\) independent users, \(M\) different kinds of transmission symbols (e.g., \(0,1,\ldots,M-1\)), and \(K\) orthogonal resource elements (REs), we propose a novel decoding scheme based on a lightweight graph neural network (GNN) with high efficiency and accuracy. The proposed decoding scheme converts the complex process of signal detection from received overlapping codewords that are affected by noise and fading into an easily implemented image classification procedure. The lightweight architecture of MobileNet [17] facilitates its deployment on mobile devices. Additionally, MobileNet's design makes it simple to expand to various widths and depths, catering to varying computational resources and accuracy requirements.