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.

该研究论文提出了一种无需依赖物理层信道反馈的传输方案，称为 RMCPNet 模型，在用户位置基础上输出基站预编码。该方案在公共 DeepMIMO 数据集上展示了相对于传统的实值神经网络和统计码本方法分别达到 16% 和 76% 的性能提升。