Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network
<|reference_start|>Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network: 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.<|reference_end|>
@article{wu2024deep,
    title={Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne
  Massive MIMO Network},
    author={Minghui Wu, Zhen Gao, Zhaocheng Wang, Dusit Niyato, George K.
  Karagiannidis, and Sheng Chen},
    journal={arXiv preprint arXiv:2405.19889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19889},
    primaryClass={eess.SP cs.IT cs.LG cs.MM math.IT}
}