标题： 基于时间信道预测的符合标准的DM-RS分配用于大规模MIMO系统 显示英文标题

标题： Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems

摘要： 在超越5G网络中减少反馈开销是一个关键挑战，因为现代大规模MIMO系统天线数量的增加显著增加了频分双工（FDD）系统中的信道状态信息（CSI）反馈需求。 为了解决这个问题，大量研究集中在CSI压缩和预测上，基于神经网络的方法正在迅速发展，并被考虑集成到3GPP 5G-Advanced标准中。 尽管深度学习已被有效应用于有限CSI的波束赋形和切换优化，但在这种约束下的参考信号分配仍然出人意料地缺乏研究。 为了填补这一空白，我们引入了基于信道预测的参考信号分配（CPRS）概念，通过联合优化信道预测和DM-RS分配来提高数据吞吐量，而无需CSI反馈。 我们进一步提出了一种符合标准的ViViT/CNN架构，通过将演变的CSI矩阵视为序列图像数据来实现CPRS，从而在动态环境中实现高效和自适应传输。 使用在NVIDIA Sionna中生成的射线追踪信道数据进行的仿真结果验证了所提出的方法，显示其吞吐量比基准策略最高提高了36.60%。 显示英文摘要

摘要： Reducing feedback overhead in beyond 5G networks is a critical challenge, as the growing number of antennas in modern massive MIMO systems substantially increases the channel state information (CSI) feedback demand in frequency division duplex (FDD) systems. To address this, extensive research has focused on CSI compression and prediction, with neural network-based approaches gaining momentum and being considered for integration into the 3GPP 5G-Advanced standards. While deep learning has been effectively applied to CSI-limited beamforming and handover optimization, reference signal allocation under such constraints remains surprisingly underexplored. To fill this gap, we introduce the concept of channel prediction-based reference signal allocation (CPRS), which jointly optimizes channel prediction and DM-RS allocation to improve data throughput without requiring CSI feedback. We further propose a standards-compliant ViViT/CNN-based architecture that implements CPRS by treating evolving CSI matrices as sequential image-like data, enabling efficient and adaptive transmission in dynamic environments. Simulation results using ray-tracing channel data generated in NVIDIA Sionna validate the proposed method, showing up to 36.60% throughput improvement over benchmark strategies.