面向真实5G NR系统的站点特定训练影响研究
On the Impact of Site-Specific Training for a Real-World 5G NR System
查看机构详情
- ETH Zurich（苏黎世联邦理工学院）
- NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本研究针对真实5G NR系统，对三类接收机架构开展站点特定微调，验证其在双上行链路传输及跨六个月测量场景下的有效性，结合特定信道估计实现最低误码率，相关代码与数据集公开。
中文摘要 AI 辅助
站点特定训练可在不增加计算复杂度的前提下提升无线接收机性能，但目前的实际研究结果多聚焦于全可训练神经接收机与单层传输场景。本研究对三种接收机架构开展站点特定微调：全可训练神经架构、模型驱动神经架构及基于模型的架构。我们利用苏黎世联邦理工学院符合标准的5G NR测试床采集的新测量数据训练并评估这些接收机，测试场景包含双上行链路传输，且测量活动间隔超过六个月。结果显示，站点特定微调：（i）可显著提升全可训练与模型驱动神经接收机，而对可调性较低的基于模型的接收机仅带来微小增益；（ii）使针对单层与双传输联合微调的单个神经接收机，性能可与分别针对各配置微调的接收机相媲美；（iii）在间隔超六个月的测量活动中仍保持有效性。我们还研究了站点特定线性最小均方误差信道估计，该方法利用合成信道或站点特定测量估计的协方差矩阵实现，结合迭代检测与解码后，站点特定信道估计在我们的数据集上达到最低误码率。本研究的微调代码与测量数据集可通过该公开URL获取。
英文摘要
Site-specific training can improve wireless receiver performance without increasing computational complexity. However, real-world results have so far focused on fully trainable neural receivers and single-layer transmissions. We study site-specific finetuning of three receiver architectures: fully trainable neural, model-driven neural, and model-based. We train and evaluate these receivers using new measurements from a standard-compliant 5G NR testbed at ETH Zurich with dual-layer uplink transmission, including measurement campaigns conducted more than six months apart. Our results show that site-specific finetuning (i) substantially improves fully trainable and model-driven neural receivers, while resulting in only marginal gains for the less tunable model-based receiver; (ii) enables a single neural receiver jointly finetuned for single- and dual-layer transmission to closely match receivers finetuned separately for each configuration; and (iii) remains effective across measurement campaigns separated by more than six months. We also investigate site-specific linear minimum mean-square error channel estimation using covariance matrices estimated from either synthetic channels or site-specific measurements. When combined with iterative detection and decoding, site-specific channel estimation achieves the lowest error rate observed in our datasets. Our finetuning code and measurement datasets are publicly available at https://github.com/IIP-Group/site_specific_training
