# Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO

arXiv: https://arxiv.org/abs/2307.03070 · HTML: https://mlsanity.com/paper/2307.03070/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 6 Jul 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019)
- [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021)
- [Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2006.02201/) (2020)
- [Deep CSI Compression for Massive MIMO: A Self-information Model-driven Neural Network](https://mlsanity.com/paper/2204.11567/) (2022)
- [Ubiquitous Cell-Free Massive MIMO Communications](https://mlsanity.com/paper/1804.03421/) (2018)
- [Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO](https://mlsanity.com/paper/1909.01034/) (2019)
- [Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms](https://mlsanity.com/paper/2007.09617/) (2020)
- [User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies](https://mlsanity.com/paper/1710.00790/) (2017)
- [Beyond 5G: Leveraging Cell Free TDD Massive MIMO using Cascaded Deep learning](https://mlsanity.com/paper/1910.05705/) (2019)
- [Distributed Precoding Design via Over-the-Air Signaling for Cell-Free Massive MIMO](https://mlsanity.com/paper/2004.00299/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network](https://mlsanity.com/paper/2405.19889/) (2024)
- [Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems](https://mlsanity.com/paper/2502.13390/) (2025)
- [Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC](https://mlsanity.com/paper/2403.10927/) (2024)

## Abstract (arXiv)

This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications, and close the gap with current indoor wireless transmission capabilities. We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems. Specifically, we firstly propose a data-driven multiple layer perceptron (MLP)-Mixer-based auto-encoder for channel semantic acquisition, where the pilot signals, CSI quantizer for channel semantic embedding, and CSI reconstruction for channel semantic extraction are jointly optimized in an end-to-end manner. Moreover, based on the acquired channel semantic, we further propose a knowledge-driven deep-unfolding multi-user beamformer, which is capable of achieving good spectral efficiency with robustness to imperfect CSI in outdoor XR scenarios. By unfolding conventional successive over-relaxation (SOR)-based linear beamforming scheme with deep learning, the proposed beamforming scheme is capable of adaptively learning the optimal parameters to accelerate convergence and improve the robustness to imperfect CSI. The proposed deep unfolding beamforming scheme can be used for access points (APs) with fully-digital array and APs with hybrid analog-digital array. Simulation results demonstrate the effectiveness of our proposed scheme in improving the accuracy of channel acquisition, as well as reducing complexity in both CSI acquisition and beamformer design. The proposed beamforming method achieves approximately 96% of the converged spectrum efficiency performance after only three iterations in downlink transmission, demonstrating its efficacy and potential to improve outdoor XR applications.