Semantic Communications With Artificial Intelligence Tasks: Reducing Bandwidth Requirements and Improving Artificial Intelligence Task Performance

Yang, Yang; Guo, Caili; Liu, Fangfang; Liu, Chuanhong; Sun, L.; Sun, Qizheng; Chen, Jiujiu

doi:10.1109/mie.2022.3174331

Cited by 28 publications

(9 citation statements)

References 22 publications

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“…Concerning wireless SemCom and goal-oriented wireless SemCom, the state-of-the-art also comprises many goaloriented wireless SemCom developments. Major trends in these developments include task-oriented communication with digital modulation [52]; goal-oriented SemCom with AI tasks [161]; intent-based goal-oriented SemCom [162], [163]; and multi-user goal-oriented SemCom [164]. The reader is referred to the vision papers [47], [49], [136], [159], and [165]- [167] and the tutorial/survey papers [39], [50], [51], [58], and [168] on goal-oriented wireless SemCom.…”

Section: Goal-oriented Wireless Semcommentioning

confidence: 99%

“…Accordingly, the data concerning any abnormal temperature increase should be assigned high VoI [58]. In this respect, the image classification task studied in [161] is assessed by the (metric) VoI pertaining to the importance of the extracted features for the accurate classification of the images. Furthermore, VoI can be employed as part of a prioritizing scheduler [51], [256].…”

Section: B Voi-based Semantic Metricsmentioning

confidence: 99%

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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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Section: Goal-oriented Wireless Semcommentioning

confidence: 99%

Section: B Voi-based Semantic Metricsmentioning

confidence: 99%

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

2023

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“…In [71], a semantic communication architecture (SC-AIT) is introduced where both effectiveness, semantic and technical level of communication is inter-connected via a neural network supported module. The proposed architecture is realized on a real-world test-bed for image classification task to detect surface defections.…”

Section: B Semantic Communicationsmentioning

confidence: 99%

Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals

Kalfa

Yetim

Atalik

et al. 2023

IEEE J. Select. Areas Commun.

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Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

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“…2, the proposed semantic importance evaluation method primarily comprises two modules: the Semantics Task Relevance (STR) module and the Inter-Semantics Relevance (ISR) module. Building on the insights from [3], semantic features that exhibit higher relevance to the task are anticipated to make more substantial contributions to the successful execution of intelligent tasks. Additionally, research conducted by [20] and [21] has substantiated that feature correlations can inform feature selection, and the use of correlation-based feature weighting can lead to improved task performance.…”

Section: Semantic Importance Evaluationmentioning

confidence: 99%

“…unnecessary depletion of wireless communication resources and falls short of addressing the burgeoning demands of future large-scale communications. Semantic communication (SemCom) [2], a technique capable of notably enhancing communication efficiency and robustness, is now acknowledged as a foundational technology driving the progression of sixthgeneration wireless networks [3].…”

Section: Introductionmentioning

confidence: 99%

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

Liu,

Nan,

Cui

et al. 2023

IEEE Commun. Mag.

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Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequencyselective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.

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Semantic Communications With Artificial Intelligence Tasks: Reducing Bandwidth Requirements and Improving Artificial Intelligence Task Performance

Cited by 28 publications

References 22 publications

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

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

Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

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