Less Data, More Knowledge: Building Next Generation Semantic
Communication Networks
- URL: http://arxiv.org/abs/2211.14343v1
- Date: Fri, 25 Nov 2022 19:03:25 GMT
- Title: Less Data, More Knowledge: Building Next Generation Semantic
Communication Networks
- Authors: Christina Chaccour, Walid Saad, Merouane Debbah, Zhu Han, H. Vincent
Poor
- Abstract summary: We present the first rigorous vision of a scalable end-to-end semantic communication network.
We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones.
By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice.
- Score: 180.82142885410238
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic communication is viewed as a revolutionary paradigm that can
potentially transform how we design and operate wireless communication systems.
However, despite a recent surge of research activities in this area, the
research landscape remains limited. In this tutorial, we present the first
rigorous vision of a scalable end-to-end semantic communication network that is
founded on novel concepts from artificial intelligence (AI), causal reasoning,
and communication theory. We first discuss how the design of semantic
communication networks requires a move from data-driven networks towards
knowledge-driven ones. Subsequently, we highlight the necessity of creating
semantic representations of data that satisfy the key properties of minimalism,
generalizability, and efficiency so as to do more with less. We then explain
how those representations can form the basis a so-called semantic language. By
using semantic representation and languages, we show that the traditional
transmitter and receiver now become a teacher and apprentice. Then, we define
the concept of reasoning by investigating the fundamentals of causal
representation learning and their role in designing semantic communication
networks. We demonstrate that reasoning faculties are majorly characterized by
the ability to capture causal and associational relationships in datastreams.
For such reasoning-driven networks, we propose novel and essential semantic
communication metrics that include new "reasoning capacity" measures that could
go beyond Shannon's bound to capture the convergence of computing and
communication. Finally, we explain how semantic communications can be scaled to
large-scale networks (6G and beyond). In a nutshell, we expect this tutorial to
provide a comprehensive reference on how to properly build, analyze, and deploy
future semantic communication networks.
Related papers
- Autoencoder-Based Domain Learning for Semantic Communication with
Conceptual Spaces [1.7404865362620803]
We develop a framework for learning a domain of a conceptual space model using only the raw data with high-level property labels.
In experiments using the MNIST and CelebA datasets, we show that the domains learned using the framework maintain semantic similarity relations and possess interpretable dimensions.
arXiv Detail & Related papers (2024-01-29T21:08:33Z) - Disentangling Learnable and Memorizable Data via Contrastive Learning
for Semantic Communications [81.10703519117465]
A novel machine reasoning framework is proposed to disentangle source data so as to make it semantic-ready.
In particular, a novel contrastive learning framework is proposed, whereby instance and cluster discrimination are performed on the data.
Deep semantic clusters of highest confidence are considered learnable, semantic-rich data.
Our simulation results showcase the superiority of our contrastive learning approach in terms of semantic impact and minimalism.
arXiv Detail & Related papers (2022-12-18T12:00:12Z) - Imitation Learning-based Implicit Semantic-aware Communication Networks:
Multi-layer Representation and Collaborative Reasoning [68.63380306259742]
Despite its promising potential, semantic communications and semantic-aware networking are still at their infancy.
We propose a novel reasoning-based implicit semantic-aware communication network architecture that allows multiple tiers of CDC and edge servers to collaborate.
We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users.
arXiv Detail & Related papers (2022-10-28T13:26:08Z) - 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) - Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic
Communication [85.06664206117088]
6G networks must consider semantics and effectiveness (at end-user) of the data transmission.
NeSy AI is proposed as a pillar for learning causal structure behind the observed data.
GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
arXiv Detail & Related papers (2022-05-22T07:11:57Z) - Semantic Communications: Principles and Challenges [59.13318519076149]
This article provides an overview on semantic communications.
After a brief review on Shannon information theory, we discuss semantic communications with theory, frameworks, and system design enabled by deep learning.
arXiv Detail & Related papers (2021-12-30T16:32:00Z) - Learning Semantics: An Opportunity for Effective 6G Communications [8.262718096663077]
semantic communications are envisioned as a key enabler of future 6G networks.
This work explores the opportunity offered by semantic communications for beyond 5G networks.
We present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications.
arXiv Detail & Related papers (2021-10-14T08:00:54Z)
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.