2 research outputs found
A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression
Nowadays, the need for high-quality image reconstruction and restoration is
more and more urgent. However, most image transmission systems may suffer from
image quality degradation or transmission interruption in the face of
interference such as channel noise and link fading. To solve this problem, a
relay communication network for semantic image transmission based on shared
feature extraction and hyperprior entropy compression (HEC) is proposed, where
the shared feature extraction technology based on Pearson correlation is
proposed to eliminate partial shared feature of extracted semantic latent
feature. In addition, the HEC technology is used to resist the effect of
channel noise and link fading and carried out respectively at the source node
and the relay node. Experimental results demonstrate that compared with other
recent research methods, the proposed system has lower transmission overhead
and higher semantic image transmission performance. Particularly, under the
same conditions, the multi-scale structural similarity (MS-SSIM) of this system
is superior to the comparison method by approximately 0.2
Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication
Semantic communication serves as a novel paradigm and attracts the broad
interest of researchers. One critical aspect of it is the multi-user semantic
communication theory, which can further promote its application to the
practical network environment. While most existing works focused on the design
of end-to-end single-user semantic transmission, a novel non-orthogonal
multiple access (NOMA)-based multi-user semantic communication system named
NOMASC is proposed in this paper. The proposed system can support semantic
tranmission of multiple users with diverse modalities of source information. To
avoid high demand for hardware, an asymmetric quantizer is employed at the end
of the semantic encoder for discretizing the continuous full-resolution
semantic feature. In addition, a neural network model is proposed for mapping
the discrete feature into self-learned symbols and accomplishing intelligent
multi-user detection (MUD) at the receiver. Simulation results demonstrate that
the proposed system holds good performance in non-orthogonal transmission of
multiple user signals and outperforms the other methods, especially at
low-to-medium SNRs. Moreover, it has high robustness under various simulation
settings and mismatched test scenarios.Comment: accepted by IEEE Transactions on Cognitive Communications and
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