# Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network arXiv: https://arxiv.org/abs/2405.19889 ยท HTML: https://mlsanity.com/paper/2405.19889/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 68 references that are in the map. - First posted on arXiv 30 May 2024. ## Builds on (most cited first, 30 of 40) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018) - [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020) - [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022) - [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021) - [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017) - [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022) - [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021) - [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023) - [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020) - [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022) - [Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis](https://mlsanity.com/paper/2205.04603/) (2022) - [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018) - [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020) - [OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels](https://mlsanity.com/paper/2109.05194/) (2021) - [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (2024) - [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019) - [SCAN: Semantic Communication with Adaptive Channel Feedback](https://mlsanity.com/paper/2306.15534/) (2023) - [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021) - [Prospective Multiple Antenna Technologies for Beyond 5G](https://mlsanity.com/paper/1910.00092/) (2019) - [Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting](https://mlsanity.com/paper/2301.01987/) (2023) - [Vision Transformer for Adaptive Image Transmission over MIMO Channels](https://mlsanity.com/paper/2210.15347/) (2022) - [Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding](https://mlsanity.com/paper/2211.04339/) (2022) - [Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI](https://mlsanity.com/paper/2201.06778/) (2022) - [Constellation Design for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.07008/) (2022) - [Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication](https://mlsanity.com/paper/2311.06770/) (2023) - [Near-Space Communications: the Last Piece of 6G Space-Air-Ground-Sea Integrated Network Puzzle](https://mlsanity.com/paper/2401.00283/) (2023) ## Built on by (most cited first, 4 of 4) - [Generative Video Semantic Communication via Multimodal Semantic Fusion with Large Model](https://mlsanity.com/paper/2502.13838/) (2025) - [E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion](https://mlsanity.com/paper/2509.19312/) (2025) - [Semantic Error Correction and Decoding for Short Block Codes](https://mlsanity.com/paper/2604.22269/) (2026) - [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026) ## Abstract (arXiv) Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships' advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches.