Context-Based Semantic Communication via Dynamic Programming

Zhang, Yichi; Zhao, Haitao; Wei, Jibo; Zhang, Jiao; Flanagan, Mark F.; Xiong, Jun

doi:10.1109/tccn.2022.3173056

Cited by 17 publications

(7 citation statements)

References 28 publications

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“…Differently, coexistence of heterogeneous tasks types has been the focus in [13], where an encoder architecture able to discern between classification and detection has been designed. Similarly, in [14] it was outlined the development of a dynamic context aware machine learning decoder to dynamically interpret the tasks type, aiming at performing proper semantic extraction.…”

Section: Related Workmentioning

confidence: 99%

A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

Picano,

Fantacci

2024

IEEE Internet Things J.

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Nowadays, an ambitious target of the next generation networks is to develop intelligent overarching spaceair-ground-aqua computing systems, in order to provide a smart ecosystem able to efficiently operate computation in heterogeneous domains. In particular, in such a context, the underwater environment requires a special attention, since it is recognized as the most challenging domain, due to channel impairments and adverse propagation conditions. This paper proposes a self-intelligent system able to efficiently perform underwater environment monitoring or underwater survey of critical infrastructure, by resorting to the use of the semantic communication paradigm to lower the impairments due to the underwater channel propagation conditions. In particular, in our case, images sent by underwater devices are collected by shore small base stations (SSBSs) to form their training dataset to take part in a federated learning process with a ground base station. In particular, the paper considers a semantic communication scheme based on a deep-convolution neural networks encoder-decoder architecture for an efficient exploitation of the data transmission from underwater devices to the linked SSBSs. Performance analysis is provided to show the better behavior of the proposed system in comparison with the conventional alternative that does not involve the use of the semantic communications approach. Finally, a specific performance evaluation analysis is devoted to the investigation of the convergence behavior of the proposed federated learning procedure in reference to the cross groundaqua system considered in order to highlight its advantages with respect to a classical implementation.

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Section: Related Workmentioning

confidence: 99%

A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

Picano,

Fantacci

2024

IEEE Internet Things J.

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“…In Table VI, we summarize the different DL-enabled semantic systems based on the different transmission tasks (text [34], [149], [150], image [151], [152], speech signals [153], [154] and general signals [155], [156]), from the perspective of performance metrics, semantic quantity module, semantic error module, loss function, KBs as well as the adaptation to dynamic environment for task-specific applications.…”

Section: Applications Of Semantic Communicationsmentioning

confidence: 99%

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Shi¹,

Lian²,

Shi³

et al. 2023

Preprint

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The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, largescale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, generalizability, computational efficiency and robustness. In this paper, we systematically review the most representative "learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-andbound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, as well as endto-end learning for semantic optimization, for solving challenging large-scale optimization problems arising from various important wireless applications. To enable ML implementation in distributed wireless networks across massive number of end devices, federated learning for distributed optimization will further be presented. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks. Neural network design, theoretical tools of different ML methods,

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“…In addition to the aforementioned wireless SemCom techniques, the rapidly evolving state-of-the-art research landscape of SemCom also encompasses numerous SemCom techniques and trends such as cognitive SemCom [122]; implicit SemCom [123]; adaptive SemCom [124]; contextbased SemCom [99], [125]; digital SemCom [126], [127]; SemCom with conceptual spaces [128]; inverse SemCom [129]; one-to-many SemCom [130]; cooperative SemCom [131]; strategic SemCom [132]; and encrypted SemCom [133]. These wireless SemCom techniques have also been corroborated to outperform traditional wireless communication techniques in low SNR regimes.…”

Section: A Wireless Semcommentioning

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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Context-Based Semantic Communication via Dynamic Programming

Cited by 17 publications

References 28 publications

A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

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

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