发表机构
University of California San Diego（加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对导频受限的上行信道估计，提出TRACE方法，利用常规多用户上行流量学习几何感知先验并结合少量导频，在15 GHz街道峡谷中以8导频实现-7.48 dB NMSE，优于现有方法超3 dB。
AI 中文摘要
可靠的无线系统依赖于基站处用于预编码、调度和链路自适应的准确上行信道估计。在上中频段，宽带宽和大规模天线阵列增加了信道维度，使得导频高效估计尤为具有挑战性。虽然上行数据在每个用户传输时都是可用的，但探测资源在小区内所有用户之间共享，导致每个用户仅有少量导频。因此，信道估计在很大程度上依赖于先验信息。现有方法以不同方式获取此类先验，但各有局限。射线追踪需要站点模型，信道知识图谱依赖于其所代表位置处或附近的测量，而学习的信道分布则未显式捕获传播几何。因此，我们提出流量辅助信道估计（TRACE），该方法无需额外探测即可从上行流量构建先验。TRACE从常规多用户上行流量中学习几何感知的信道先验，并将其与可用导频相结合进行信道估计。在15 GHz的射线追踪街道峡谷场景中，TRACE在8个导频下实现了-7.48 dB的归一化均方误差（NMSE），优于三种最先进的先验方法和两种几何消融方法超过3 dB，同时在仅2个导频时仍保持有效。在部署中，TRACE在无需重新训练的情况下，跨信噪比、载波频率和减少的映射流量保持稳健。
英文摘要
Reliable wireless systems depend on accurate uplink channel estimation for precoding, scheduling, and link adaptation at the base station. In the upper mid-band, wide bandwidths and large antenna arrays increase the channel dimension and make pilot-efficient estimation particularly challenging. While uplink data is available as every user transmits it, sounding resources are shared across all users in the cell, leaving only a few pilots for each user. Thus, channel estimation heavily depends on prior information. Existing approaches obtain such priors in different ways, each with limitations. Ray tracing requires a site model, channel knowledge maps rely on measurements at or near the locations they represent, and learned channel distributions do not explicitly capture propagation geometry. We therefore propose TRaffic-Aided Channel Estimation (TRACE), which builds the prior from uplink traffic without additional sounding. TRACE learns a geometry-aware channel prior from routine multi-UE uplink traffic and combines it with the available pilots for channel estimation. In a ray-traced street canyon at 15 GHz, TRACE achieves -7.48 dB NMSE with 8 pilots, outperforming three state-of-the-art priors and two geometric ablations by more than 3 dB, while remaining effective with only 2 pilots. Within a deployment, TRACE remains robust across SNRs, carrier frequencies, and reduced mapping traffic without retraining.
CommentsThis work has been submitted to the IEEE for possible publication
