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

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach