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

arXiv: https://arxiv.org/abs/2312.02184 · HTML: https://mlsanity.com/paper/2312.02184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2003.04684/) (2020)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks](https://mlsanity.com/paper/1810.07181/) (2018)
- [Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks](https://mlsanity.com/paper/2108.11540/) (2021)
- [Deep CSI Compression for Massive MIMO: A Self-information Model-driven Neural Network](https://mlsanity.com/paper/2204.11567/) (2022)
- [Learning Robust Beamforming for MISO Downlink Systems](https://mlsanity.com/paper/2103.01602/) (2021)
- [A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels](https://mlsanity.com/paper/2001.07054/) (2020)
- [High-Efficiency Device Positioning and Location-Aware Communications in Dense 5G Networks](https://mlsanity.com/paper/1608.03775/) (2016)
- [Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach](https://mlsanity.com/paper/2009.07478/) (2020)
- [IMRecoNet: Learn to Detect in Index Modulation Aided MIMO Systems with Complex Valued Neural Networks](https://mlsanity.com/paper/2112.00910/) (2021)

## Built on by (most cited first, 1 of 1)

- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)

## Abstract (arXiv)

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.