ReQuestNet统一处理5G多场景信道估计，性能远超传统方法。
ReQuestNet: A Foundational Learning model for Channel Estimation
- 用两阶段网络联合处理不同预编码和多天线相关性
- 高信噪比下比理想MMSE提升10dB，泛化能力出色
- 适合5G/6G动态资源分配场景，工程部署更简单
本文提出一种面向5G及未来通信系统的新型神经架构——递归等变UERS估计网络（ReQuestNet），可统一处理可变资源块数量、动态发射层数、物理资源块组捆绑大小（BS）以及单参考信号模式，显著简化信道估计流程。相比传统线性最小均方误差（MMSE）方案，其不依赖其他参考信号，能联合处理多输入多输出（MIMO）层与未知预编码的信道。ReQuestNet由粗估计网络（CoarseNet）和精炼网络（RefinementNet）组成：CoarseNet在每个物理资源块组（PRG）和发射-接收流上独立估计信道；RefinementNet则利用不同预编码的PRG间相关性及多天线空间维度间的跨MIMO相关性进行优化。仿真表明，ReQuestNet在多种信道条件和时延-多普勒分布下均显著优于理想MMSE基准，在高信噪比下最高实现10dB增益。其对未见信道配置具有良好泛化能力，能高效利用跨PRG与跨MIMO的相关性，适用于动态捆绑大小与发射层数变化的场景。
原文摘要 · Abstract (English)
In this paper, we present a novel neural architecture for channel estimation (CE) in 5G and beyond, the Recurrent Equivariant UERS Estimation Network (ReQuestNet). It incorporates several practical considerations in wireless communication systems, such as ability to handle variable number of resource block (RB), dynamic number of transmit layers, physical resource block groups (PRGs) bundling size (BS), demodulation reference signal (DMRS) patterns with a single unified model, thereby, drastically simplifying the CE pipeline. Besides it addresses several limitations of the legacy linear MMSE solutions, for example, by being independent of other reference signals and particularly by jointly processing MIMO layers and differently precoded channels with unknown precoding at the receiver. ReQuestNet comprises of two sub-units, CoarseNet followed by RefinementNet. CoarseNet performs per PRG, per transmit-receive (Tx-Rx) stream channel estimation, while RefinementNet refines the CoarseNet channel estimate by incorporating correlations across differently precoded PRGs, and correlation across multiple input multiple output (MIMO) channel spatial dimensions (cross-MIMO). Simulation results demonstrate that ReQuestNet significantly outperforms genie minimum mean squared error (MMSE) CE across a wide range of channel conditions, delay-Doppler profiles, achieving up to 10dB gain at high SNRs. Notably, ReQuestNet generalizes effectively to unseen channel profiles, efficiently exploiting inter-PRG and cross-MIMO correlations under dynamic PRG BS and varying transmit layer allocations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。