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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. RM-CPNet comprises multiple subnets, with each subnet responsible for extracting unique modal features from diverse input modalities. Furthermore, the multimodal 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 RMCP-Net can achieve 16% and 76% performance improvements over the conventional real-valued neural network and statistical codebook approach, respectively.

IoV (Internet of Vehicles) is a promising paradigm to the future of automobiles, which will undoubtedly boost the automobile market as well as accelerate innovation in Internet services and applications. The concept of SD-IoV (Software Defined IoV) is presented, which is capable of improving resource utilization, service quality, and network optimization in the harsh vehicular network environments. First, A generalized SD-IoV architecture as an intuitive big picture is presented. Then, the major functions realized by SD-IoV are elabrated on to illustrate how the current challenges are resolved. As the key enablers of SD-IoV, three possible implementation methods of the wireless control path are described and compared. Finally, the challenges and existing solutions of SD-IoV are disuessed and open issues are pointed out so as to shed light on future research.