Cognitive Semantic Communication Systems Driven by Knowledge Graph

Zhou, Fuhui; Li, Yihao; Zhang, Xinyuan; Wu, Qihui; Lei, Xianfu; Hu, Rose Qingyang

doi:10.48550/arxiv.2202.11958

Cited by 2 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Knowledge-Aware Semantic Communication System Design and Data Allocation

Kadam¹,

Kim²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where C L m,n is the listener's incurred cost for the inferred description at slot n of episode m, as per (5). Similar to the speaker's case, one can easily see that the reward function in ( 23) is defined in a way to solve problem (11).…”

Section: ) Learning During the First CLmentioning

confidence: 99%

“…method for improving the accuracy of semantic-based audio communication. The main drawback of [8]- [13] is that their models are only applicable to specific data types and, like [5]- [7], they do not consider the effectiveness of the semantics on the system's goal and, thus, they cannot be generalized to broader goal-oriented semantic communication scenarios.…”

mentioning

confidence: 99%

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

Farshbafan¹,

Saad²,

Debbah³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Cognitive Semantic Communication Systems Driven by Knowledge Graph

Cited by 2 publications

References 14 publications

Knowledge-Aware Semantic Communication System Design and Data Allocation

Knowledge-Aware Semantic Communication System Design and Data Allocation

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

Contact Info

Product

Resources

About