Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

Yun, Won Joon; Lim, Byungju; Jung, Soyi; Ko, Young-Chai; Park, Jihong; Kim, Joongheon; Bennis, Mehdi

doi:10.1109/iswcs49558.2021.9562230

Cited by 34 publications

(16 citation statements)

References 11 publications

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“…4(b)). Following the idea of inter-agent knowledge sharing, [11] proposes a graph attention exchange network enabled by SemCom to share the local information between the DRL agents of UAVs. A brief structure of CDRL is shown in Fig.…”

Section: A Semantic-aware Intelligent Agentmentioning

confidence: 99%

Semantic Communication Meets Edge Intelligence

Yang¹,

Liew²,

Lim³

et al. 2022

Preprint

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The development of emerging applications, such as autonomous transportation systems, are expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a "transmit-before-understanding" approach while the latter leverages artificial intelligence (AI) techniques to "understand-before-transmit", thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT).

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Section: A Semantic-aware Intelligent Agentmentioning

confidence: 99%

Semantic Communication Meets Edge Intelligence

Yang¹,

Liew²,

Lim³

et al. 2022

Preprint

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“…Spurred by advances in ML, the problem has recently been revisited through the lens of the semantics-empowered communication framework that is broadly categorized into three directions. The first direction is to filter out less important or uninformative data, and generate semantically meaningful information at the sender [6]- [9]. Here, the importance of data can be evaluated with goal-oriented metrics considering the effectiveness of information at the receiver, such as the age-of-information-based metrics (and variants) [7], [8], attention-based similarities [9], and control-theoretic accuracy [6].…”

Section: A Related Workmentioning

confidence: 99%

Semantics-Native Communication with Contextual Reasoning

Seo¹,

Park²,

Bennis³

et al. 2021

Preprint

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Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks. In this article, inspired by human communication, we propose a novel stochastic model of System 1 semantics-native communication (SNC) for generic tasks, where a speaker has an intention of referring to an entity, extracts the semantics, and communicates its symbolic representation to a target listener. To further reach its full potential, we additionally infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the physical listener's unique way of coding its semantics, i.e., communication context. The resultant System 2 SNC allows the speaker to extract the most effective semantics for its listener. Leveraging the proposed stochastic model, we show that the reliability of System 2 SNC increases with the number of meaningful concepts, and derive the expected semantic representation (SR) bit length which quantifies the extracted effective semantics. It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.H. Seo and M. Bennis are with the Centre for Wireless Communications

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“…• Distributed consensus: Local-sate estimation and prediction [93][94][95], SDT [96], PBFT consensus [97], Vehicle platooning [98][99][100], Blockchain [96,101].…”

Section: M2m Semcommentioning

confidence: 99%

“…Other SemCom techniques have been extensively studied in the literature. First, URLLC connectivity is required in this mission-critical application to avoid collisions [99]. In terms of latency for vehicle platooning, it should be measured and minimized in terms of information latency rather than the conventional over-the-air latency as the former directly relates to coordinated control performance [100].…”

Section: Vehicle Platooningmentioning

confidence: 99%

“…To overcome the limit of radio resources, its effectiveness allocation for vehicular platooning should be importance aware by identifying critical and less critical information in vehicles' state data, allowing them to be compressed accordingly to the specific driving algorithm [98]. On the other hand, it is proposed in [99] that effectiveness RRM and multi-access should also have situation awareness and be optimized for a specific vehicular network topology represented using a graph. Based on the principles, SemCom techniques are designed based multi-agent RL to integrate the operations in the semantic layer and physical layer (e.g., transmission stopping), thereby reducing the intensity of communication.…”

Section: Vehicle Platooningmentioning

confidence: 99%

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What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

Li¹,

Wen²,

Zhang³

et al. 2021

Preprint

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In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-Things (IoT) and artificial intelligence (AI).By broadening the scope of the classic communication-theoretic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and "dialogue". On the other hand, M2M SemCom refers to effectiveness techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: (1) layer-coupling design; and (2) end-to-end design using a neural network. The second part focuses on discussion of specific techniques for different application areas of H2M SemCom (including human and AI symbiosis, recommendation, human sensing and care, and virtual reality (VR)/augmented reality (AR)) and M2M SemCom (including distributed learning, split inference, distributed consensus, and machine-vision cameras). Finally, we discuss the approach for designing SemCom systems based on knowledge graphs. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation (6G) featuring connected intelligence and integrated sensing, computing, communication, and control.

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Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

Cited by 34 publications

References 11 publications

Semantic Communication Meets Edge Intelligence

Semantic Communication Meets Edge Intelligence

Semantics-Native Communication with Contextual Reasoning

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

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