At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

• URL: http://arxiv.org/abs/2402.18587v1
• Date: Fri, 2 Feb 2024 06:23:25 GMT
• Title: At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence
• Authors: Abdulkadir Celik, Ahmed M. Eltawil
• Abstract summary: Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. We outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy
• Score: 11.847999494242387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities. Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-the-art DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including physical layer design; network optimization, organization, and management; network traffic analytics; cross-layer network security; and localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies.

Related papers

• Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy? [17.59973153669422]
  This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. GenAI can serve a function in saving energy and spectrum in wireless networks.
  arXiv  Detail & Related papers  (2024-07-02T14:27:06Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning [30.74259663690069]
  Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence. Current wireless networks are designed as a "data pipe" and are not suited to accommodate and leverage the power of GenAI. We propose the GenAINet framework in which distributed GenAI agents communicate knowledge to accomplish arbitrary tasks.
  arXiv  Detail & Related papers  (2024-02-26T15:03:46Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• Large Generative AI Models for Telecom: The Next Big Thing? [7.36678071967351]
  Large GenAI models are envisioned to open up a new era of autonomous wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating large GenAI models into the Telecom domain.
  arXiv  Detail & Related papers  (2023-06-17T03:45:00Z)
• Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study [71.8330148641267]
  Deep generation models (DGMs) have been experiencing explosive growth from 2022. In this article, we explore the applications of DGMs in improving the efficiency of wireless network management.
  arXiv  Detail & Related papers  (2023-03-30T02:59:51Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• On-Device Domain Generalization [93.79736882489982]
  Domain generalization is critical to on-device machine learning applications. We find that knowledge distillation is a strong candidate for solving the problem. We propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data.
  arXiv  Detail & Related papers  (2022-09-15T17:59:31Z)
• Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial [29.76086936463468]
  This tutorial focuses on the role of Deep Reinforcement Learning (DRL) with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. We provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL.
  arXiv  Detail & Related papers  (2020-11-06T22:12:40Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
  Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data. Such enormous data cannot all be sent from end devices to the cloud for processing. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
  arXiv  Detail & Related papers  (2020-02-22T09:27:55Z)

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.