As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs. However, with the decoupling of uplink base stations and downlink base stations in FD-RAN, the traditional transmission mechanism, which relies on real-time channel feedback, is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter. This paper proposes a novel transmission scheme without relying on physical layer channel feedback. Specifically, we design a radio map based complex-valued precoding network~(RMCPNet) model, which outputs the base station precoding based on user location. RMCPNet comprises multiple subnets, with each subnet responsible for extracting unique modal features from diverse input modalities. Furthermore, the multi-modal embeddings derived from these distinct subnets are integrated within the information fusion layer, culminating in a unified representation. We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function. We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16\% and 76\% performance improvements over the conventional real-valued neural network and statistical codebook approach, respectively.

翻译：随着高质量服务需求的激增，一种创新的网络架构——全解耦无线接入网（FD-RAN）应运而生，旨在实现更灵活的频谱资源利用和更低的网络成本。然而，在FD-RAN中上行基站与下行基站解耦后，传统依赖实时信道反馈的传输机制不再适用，因为接收端无法向发送端反馈准确及时的信道状态信息。本文提出了一种不依赖物理层信道反馈的新型传输方案。具体而言，我们设计了一种基于无线地图的复值预编码网络（RMCPNet）模型，该模型根据用户位置输出基站预编码。RMCPNet由多个子网组成，每个子网负责从不同输入模态中提取独特的模态特征。此外，这些不同子网生成的多模态嵌入在信息融合层中整合，最终形成统一表征。我们还开发了特定的RMCPNet训练算法，采用负频谱效率作为损失函数。我们在公共DeepMIMO数据集上评估了所提方案的性能，结果表明RMCPNet相比传统实值神经网络和统计码本方法，分别可实现16%和76%的性能提升。