通过预测信道优化参考信号分配，无需反馈即提升大规模MIMO吞吐量。
Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems
- 将信道预测与参考信号分配联合优化，减少对信道状态反馈的依赖。
- 在射线追踪数据下，吞吐量最高提升36.60%，优于基准策略。
- 符合3GPP标准，适用于动态环境下的5G-Advanced系统部署。
在后5G网络中，降低反馈开销是一个关键挑战，因为现代大规模MIMO系统中天线数量的增加显著提高了频分双工（FDD）系统中的信道状态信息（CSI）反馈需求。为应对这一问题，大量研究聚焦于CSI压缩与预测，基于神经网络的方法正获得关注，并被考虑纳入3GPP 5G-Advanced标准。尽管深度学习已有效应用于受限CSI的波束赋形和切换优化，但在该约束下的参考信号分配仍鲜有研究。为此，我们提出信道预测驱动的参考信号分配（CPRS）概念，通过联合优化信道预测与解调参考信号（DM-RS）分配，在无需CSI反馈的前提下提升数据吞吐量。我们进一步提出一种符合标准的ViViT/CNN架构，将随时间演化的CSI矩阵视为类图像序列数据进行处理，实现高效自适应传输。利用NVIDIA Sionna生成的射线追踪信道数据进行仿真验证，结果表明，所提方法相比基准策略最高可实现36.60%的吞吐量提升。
原文摘要 · Abstract (English)
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.
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