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

• URL: http://arxiv.org/abs/2111.08051v1
• Date: Mon, 15 Nov 2021 19:13:55 GMT
• Title: Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework
• Authors: Mohammad Karimzadeh Farshbafan, Walid Saad, and Merouane Debbah
• Abstract summary: A comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. 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.
• Score: 66.81698651016444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Text-Video Retrieval with Global-Local Semantic Consistent Learning [122.15339128463715]
  We propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL) GLSCL capitalizes on latent shared semantics across modalities for text-video retrieval. Our method achieves comparable performance with SOTA as well as being nearly 220 times faster in terms of computational cost.
  arXiv  Detail & Related papers  (2024-05-21T11:59:36Z)
• Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model [57.78191634042409]
  We propose Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level targets into the training process. Our experimental results on four spoken language understanding (SLU) benchmarks suggest the superiority of our model in capturing semantic information.
  arXiv  Detail & Related papers  (2024-02-08T16:55:21Z)
• PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models [29.016842120305892]
  This paper proposes an image pragmatic communication framework based on a Pragmatic Agent for Communication Efficiency (PACE) using Large Language Models (LLM) PACE sequentially performs semantic perception, intention resolution, and intention-oriented coding. For experimental validation, this paper constructs an image pragmatic communication dataset along with corresponding evaluation standards.
  arXiv  Detail & Related papers  (2024-01-30T06:55:17Z)
• Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors [3.266331042379877]
  In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This paper proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.
  arXiv  Detail & Related papers  (2024-01-19T16:43:47Z)
• Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework [124.6509194665514]
  A novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A projection-based semantic encoder is proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. A generative adversarial imitation learning-based solution, called G-RML, is proposed to enable the destination user to learn and imitate the implicit semantic reasoning process of source user.
  arXiv  Detail & Related papers  (2023-06-20T01:32:27Z)
• Learning to Communicate with Intent: An Introduction [2.007345596217044]
  We propose a novel framework to learn how to transmit messages over a wireless communication channel based on the end-goal of the communication. This stays in stark contrast to classical communication systems where the objective is to reproduce at the receiver side either exactly or approximately the message sent by the transmitter.
  arXiv  Detail & Related papers  (2022-11-17T16:02:13Z)
• Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications [88.68461721069433]
  Next generation systems can be potentially enriched by folding message semantics and goals of communication into their design. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.
  arXiv  Detail & Related papers  (2022-07-19T16:00:57Z)
• Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language [60.85719227557608]
  A holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. An optimization problem is defined to determine the perfect and abstract description of the events.
  arXiv  Detail & Related papers  (2022-04-21T22:36:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.