In 5G networks, non-orthogonal multiple access (NOMA) provides a number of benefits by providing uneven power distribution to multiple users at once. On the other hand, effective power allocation, successful successive interference cancellation (SIC), and user fairness all depend on precise channel state information (CSI). Because of dynamic channels, imperfect models, and feedback overhead, CSI prediction in NOMA is difficult. Our aim is to propose a CSI prediction technique based on an ML model that accounts for partially decoded data (PDD), a byproduct of the SIC process. Our proposed technique has been shown to be efficient in handover failure (HOF) prediction and reducing pilot overhead, which is particularly important in 5G. We have shown how machine learning (ML) models may be used to forecast CSI in NOMA handover.

翻译：在5G网络中，非正交多址接入技术通过同时为多个用户提供非均匀功率分配，带来了诸多优势。然而，有效的功率分配、成功的连续干扰消除以及用户公平性均依赖于精确的信道状态信息。由于信道动态变化、模型不完善及反馈开销等问题，非正交多址系统中的信道状态信息预测颇具挑战。本文旨在提出一种基于机器学习模型的信道状态信息预测技术，该技术利用了连续干扰消除过程中产生的副产品——部分解码数据。所提方法在切换失败预测和降低导频开销方面表现出显著效能，这对5G系统尤为重要。我们进一步展示了如何利用机器学习模型预测非正交多址切换过程中的信道状态信息。