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.

翻译：近空间飞艇载通信网络凭借飞艇在平流层长期驻留的优势，被认为是未来空天地一体化网络中不可或缺的组成部分，但其迫切需要可靠高效的飞艇至多类节点链路。为提升传输效率与容量，本文提出将语义通信与大规模多输入多输出技术相融合。具体而言，我们针对空间飞艇载大规模MIMO图像传输网络，提出一种深度联合语义编码与波束成形方案，该方案融合来自信源与信道的语义信息，以联合设计语义编码与物理层波束成形。首先，我们设计两个语义提取网络，分别从图像信源与信道状态信息中提取语义。随后，提出一种语义融合网络，能够将这些语义融合为复数值语义特征以供后续物理层传输。为在物理层高效传输融合后的语义特征，我们进一步提出混合数据与模型驱动的语义感知波束成形网络。在接收端，设计语义解码网络以重建传输图像。最后，我们采用端到端深度学习联合训练所有模块，并以接收端图像重建质量为评估指标。所提出的深度联合语义编码与波束成形方案充分结合了语义通信的高效信源可压缩性、强健纠错能力以及大规模MIMO的高频谱效率，相比现有方法实现了显著的性能提升。