Reliable Semantic Communication System Enabled by Knowledge Graph

Jiang, Shengteng; Liu, Yueling; Zhang, Yichi; Luo, Peng; Cao, Kuo; Xiong, Jun; Zhao, Haitao; Wei, Jibo

doi:10.3390/e24060846

Cited by 35 publications

(19 citation statements)

References 44 publications

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“…p(0) = 0 corroborate -whenever σ 2 → ∞ and P s max → 0 -that η(s, ŝ) = 0, which affirms maximum semantic dissimilarity or semantic irrelevance [31]. Consequently, Theorem 1 translates to the following remarks.…”

Section: Performance Limits Of Deepscsupporting

confidence: 58%

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

Getu¹,

Saad²,

Kaddoum³

et al. 2023

Preprint

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Semantic communication (SemCom) has emerged as a 6G enabler while promising to minimize power usage, bandwidth consumption, and transmission delay by minimizing irrelevant information transmission. However, the benefits of such a semantic-centric design can be limited by radio frequency interference (RFI) that causes substantial semantic noise. The impact of semantic noise due to interference can be alleviated using an interference-resistant and robust (IR 2 ) SemCom design. Nevertheless, no such design exists yet. To shed light on this knowledge gap and stimulate fundamental research on IR 2 SemCom, the performance limits of a text SemCom system named DeepSC is studied in the presence of single-and multi-interferer RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of single-and multi-interferer RFI gets very large. Corroborated by Monte Carlo simulations, these performance limits offer design insights -regarding IR 2 SemCom -contrary to the theoretically unsubstantiated sentiment that SemCom techniques (such as DeepSC) work well in very low signal-to-noise ratio regimes. The performance limits also reveal the vulnerability of DeepSC and SemCom to a wireless attack using RFI. Furthermore, our introduced probabilistic framework inspires the performance analysis of many text SemCom techniques, as they are chiefly inspired by DeepSC.

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Section: Performance Limits Of Deepscsupporting

confidence: 58%

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

Getu¹,

Saad²,

Kaddoum³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where 0 ≤ η(ŝ, s) ≤ 1 and B Φ (•) denotes the output of BERT, which is an enormous pre-trained model that encompasses billions of parameters used for mining semantic information [46]. As defined in (13), the metric η(s, ŝ) takes values between 0 and 1 (which mirror semantic irrelevance and semantic consistency, respectively) [174]. Meanwhile, since BERT are sensitive to polysemy, semantic information is quantified by the sentence similarity metric at the sentence level [135].…”

Section: E Semantic Similarity Metricmentioning

confidence: 99%

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

2023

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Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -scattered in many references -of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

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“…In this framework, semantic information is represented by a KG made up of a set of semantic triples and the receiver recovers the original text using a graph-to-text generation model. Another SemCom system based on the KG is proposed in [30]. In this system, transmitted sentences are converted into triplets using the KG, which are seen as fundamental semantic symbols for semantic extraction and restoration, and they are ordered based on semantic relevance.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, a method to generate a summary of sentences by using a knowledge base is shown in [35]. Recently, KGs are utlized in the context of SemCom design [29], [30], [36], [37]. But these works do not focus on the issue presented in this paper, which is to design a SemCom system with a significant overhead reduction with a little compromise on accuracy.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Aware Semantic Communication System Design and Data Allocation

Kadam¹,

Kim²

2023

Preprint

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The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to overcome the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems that have applications in the metaverse, healthcare, economics, etc. In SemCom systems, only the relevant keywords from the data are extracted and used for transmission. In this paper, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. This SemCom system is used in a setup in which the receiver allocates various categories of the same dataset collected from the transmitter, which differ in size and accuracy, to a number of users. This scenario is formulated using an optimization problem called the data allocation problem (DAP). We show that it is NP-complete and propose a greedy algorithm to solve it. Using simulations, we show that the proposed methods for SemCom system design outperform state-of-the-art methods in terms of average number of words per sentence for a given accuracy, and that the proposed greedy algorithm solution of the DAP performs significantly close to the optimal solution.

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Reliable Semantic Communication System Enabled by Knowledge Graph

Cited by 35 publications

References 44 publications

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

Knowledge-Aware Semantic Communication System Design and Data Allocation

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