Sparse code multiple access (SCMA) is one of the competitive non-orthogonal multiple access techniques for the next generation multiple access systems. One of the main challenges is high computational complexity and the SCMA-aided codewords, that is, each terminal device maintains its local data and codewords, which provides no incentive for model updating to accommodate rapidly changing vehicle communication environment. Federated learning (FL) proves its effectiveness by enabling terminals to collaboratively train their local neural network models with private data while protecting the individual SCMA-aided codewords. To select reliable and trusted codewords, this article provides an overview of the salient characteristics of the application of federated learning-driven SCMA for vehicular communication and discusses its fundamental research challenges. Furthermore, we outline the advancement of federated learning-driven SCMA schemes and present a general framework with potential solutions to the challenges. Finally, several future research directions and open issues are discussed regarding federated learning-driven SCMA schemes.

中文翻译：

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V2X 通信中联合学习驱动的稀疏代码多路访问


稀疏码多址（SCMA）是下一代多址系统的竞争性非正交多址技术之一。主要挑战之一是高计算复杂度和 SCMA 辅助码字，即每个终端设备维护其本地数据和码字，这没有为模型更新提供动力以适应快速变化的车辆通信环境。联邦学习 (FL) 使终端能够使用私有数据协作训练其本地神经网络模型，同时保护各个 SCMA 辅助码字，从而证明了其有效性。为了选择可靠且可信的码字，本文概述了联邦学习驱动的 SCMA 在车辆通信中应用的显着特征，并讨论了其基础研究挑战。此外，我们概述了联合学习驱动的 SCMA 方案的进展，并提出了一个通用框架，其中包含应对挑战的潜在解决方案。最后，讨论了关于联邦学习驱动的 SCMA 方案的几个未来研究方向和开放问题。