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:10.48550/arxiv.2105.10716

Cited by 4 publications

(9 citation statements)

References 14 publications

(16 reference statements)

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“…Fortunately, because SemCom can reduce the amount of information that needs to be transmitted, an efficient communication framework among UAVs can be implemented. A novel centralized training and decentralized execution (CTDE) of multi-agent DRL (MADRL) framework is proposed for UAV-assisted URLLC [71]. Compared to the state-of-theart CTDE method, the proposed solution, graph attention exchange network (GAXNet), achieves 6.5x lower latency with the target 10 −7 error rate by exchanging semantically meaningful attention weights [118].…”

Section: F Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…Moreover, for the real-time remote tracking scenario in [115], the SE can be regarded as the sampling actions, which depends on the transition matrix of discrete-time Markov chain for the source's states. Meanwhile, in [71], the authors study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC). In the proposed scheme, each agent locally constructs a startopological graph, where the semantic information generated in agent interactions is encoded as the edge weight, which reflects the level of attention to the leaf agent when the internal agent takes its action.…”

Section: A Accurate Sementioning

confidence: 99%

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Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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Section: F Unmanned Aerial Vehiclesmentioning

confidence: 99%

Section: A Accurate Sementioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In [5], the authors proposed a reinforcement learning (RL) method to capture the meaning of transmitted information by learning se-mantic similarities between them. The work in [6] introduced a model for implementing semantic communications to address the reliability and latency requirements for drone networks. The work in [7] introduced a neural agent architecture with the capability of communication among the agents using discrete tokens.…”

Section: Introductionmentioning

confidence: 99%

Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

Farshbafan¹,

Saad²,

Debbah³

2021

Preprint

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Semantic communications will play a critical role in enabling goal-oriented services over next-generation wireless systems. However, most prior art in this domain is restricted to specific applications (e.g., text or image), and it does not enable goal-oriented communications in which the effectiveness of the transmitted information must be considered along with the semantics so as to execute a certain task. In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. To capture the semantics between a speaker and a listener, a common language is defined using the concept of beliefs to enable the speaker to describe the environment observations to the listener. Then, an optimization problem is posed to choose the minimum set of beliefs that perfectly describes the observation while minimizing the task execution time and transmission cost. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.

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mentioning

confidence: 99%

“…The work in [15] introduced a model for implementing semantic communications to address the reliability and latency requirements for drone networks. The works in [14] and [15] can be considered as task-oriented semantic communication approaches. However, their solutions are only applicable to the specific IoT applications that they considered and, thus, they cannot be generalized.…”

mentioning

confidence: 99%

Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language

Farshbafan¹,

Saad²,

Debbah³

2022

Preprint

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Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the transmission and inference costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution cost and time, reliability, and belief efficiency.

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

Cited by 4 publications

References 14 publications

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language

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