Autoencoder-based Semantic Communication Systems with Relay Channels

Luo, Xinlai; Chen, Zhiyong; Xia, Bin; Wang, Jiangzhou

doi:10.1109/iccworkshops53468.2022.9814566

Cited by 24 publications

(16 citation statements)

References 11 publications

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“…In this case, the training loss function was modified to take into account both users. Finally, in [24], a different kind of DNN was implemented and combined with a forward relay to improve the common base knowledge between transmitter and receiver, which in general can be different. In this case, an autoencoder structure was again used for the end-to-end training of the proposed model.…”

Section: A Literature Reviewmentioning

confidence: 99%

Hybrid Semantic-Shannon Communications

Evgenidis¹,

Mitsiou²,

Tegos³

et al. 2023

Preprint

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<p>Recently, semantic communications have been considered as a promising beyond-Shannon paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable “similarity” between the transmitted and the received signal. If a strict similarity requirement holds, semantic transmission may not be applicable, thus Shannon communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrier Hybrid Semantic-Shannon communication system, where without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-Shannon transmission selection and power allocation optimization problem is formulated, aiming to minimize the transmission delay, while it guarantees a strict similarity threshold. Despite its non-convexity, by utilizing alternating optimization the problem is separated into a convex and a mixed linear integer programming one, both of which can be optimally solved. Moreover, to improve the performance of the proposed hybrid scheme, a novel association of text sentences to subcarriers is proposed based on the sentence’s data size and subcarriers’ channel gains. We show that the proposed association is optimal in terms of transmission time delay. Numerical results and simulations verify the effectiveness of the proposed hybrid semantic-Shannon communication method and the derived sentence-to-subcarrier association, and provide useful insights on the designing parameters of such systems. </p>

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Section: A Literature Reviewmentioning

confidence: 99%

Hybrid Semantic-Shannon Communications

Evgenidis¹,

Mitsiou²,

Tegos³

et al. 2023

Preprint

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“…In this paper, we use the transformer network as the semantic codec [10], which performs well on text tasks. Autoencoder can be used as the channel codec [5].…”

Section: B Encrypted Semantic Communication Systemmentioning

confidence: 99%

“…The following description only presents the network parameters that need to be updated in the loss function. The embedding layer is fixed and the channel encoding and decoding networks are pre-trained [5]. For the distance function, we use cross-entropy D CE , which can be formulated as…”

Section: A Loss Function Designmentioning

confidence: 99%

“…In [4], the authors design an end-to-end (E2E) model for textual semantic communication and apply transfer learning to the training process, significantly reducing the training time. The work [5] proposes a relay semantic communication model and designs a semantic-andforward (SF) scheme to solve the heterogeneous background knowledge problem of E2E semantic communication, but it lacks the protection of privacy. In [6], a data adaptation network is proposed to solve the problem of background knowledge heterogeneity and protect the privacy of the pragmatic use of transmitted image data at the receiver.…”

mentioning

confidence: 99%

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Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving

Luo¹,

Chen²,

Tao³

et al. 2022

Preprint

Self Cite

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Semantic communication is implemented based on shared background knowledge, but the sharing mechanism risks privacy leakage. In this letter, we propose an encrypted semantic communication system (ESCS) for privacy preserving, which combines universality and confidentiality. The universality is reflected in that all network modules of the proposed ESCS are trained based on a shared database, which is suitable for large-scale deployment in practical scenarios. Meanwhile, the confidentiality is achieved by symmetric encryption. Based on the adversarial training, we design an adversarial encryption training scheme to guarantee the accuracy of semantic communication in both encrypted and unencrypted modes. Experiment results show that the proposed ESCS with the adversarial encryption training scheme can perform well regardless of whether the semantic information is encrypted. It is difficult for the attacker to reconstruct the original semantic information from the eavesdropped message.

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“…Firstly, most existing studies on semantic communication [9]- [11] focus on relatively simple point-to-point (P2P) communication scenarios, and it remains a challenge to design efficient semantic communication systems on cooperative multi-point networks [16]. On the other hand, although semantic communication has been applied to fixed relay channels [17], [18], how to effectively improve the system performance by merging the multiple semantic information flows still remains a major challenge. Furthermore, while most existing studies focus solely on measuring the semantic fidelity [9]- [11], it remains a challenge to characterize the trade-off between different performance metrics, e.g., semantic fidelity and system energy consumption.…”

Section: Introduction a Motivationmentioning

confidence: 99%

Cooperative Semantic Communication With On-Demand Semantic Forwarding

Tang,

Huang,

et al. 2024

IEEE Open J. Commun. Soc.

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In this paper, a deep learning-based cooperative semantic communication system is proposed on relay channels. In order to enhance the reliability and adaptability of the system to varying channel conditions, an on-demand semantic forwarding framework is established, where the relay attempts to recover and re-transmit the source semantic information, as required by the destination. To be specific, for determining whether a semantic forwarding is needed from the relay, a semantic similarity check (SSC) is proposed, based on which the degree of semantic information recovery at the destination can be accurately estimated. On the other hand, in order to effectively merge the semantic information received through different paths, a semantic combining (SeC) method is proposed by combining the semantic features abstracted from both the direct link and the relay link. For achieving a desirable performance trade-off between the degree of semantic information recovery and the transmit energy consumption, a new metric of semantic energy efficiency (SEE) is proposed. Simulation results verify the performance gains achieved by the proposed cooperative semantic communication system with on-demand semantic forwarding, as compared to the state-of-the-art schemes employing separate source-channel coding in low-to-medium signal-to-noise ratio (SNR) regimes. Furthermore, as compared to the case with alwaysforwarding, almost the same performance is achieved by the proposed on-demand forwarding, but with energy consumption.

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Autoencoder-based Semantic Communication Systems with Relay Channels

Cited by 24 publications

References 11 publications

Hybrid Semantic-Shannon Communications

Hybrid Semantic-Shannon Communications

Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving

Cooperative Semantic Communication With On-Demand Semantic Forwarding

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