Task-Oriented Integrated Sensing, Computation and Communication for
Wireless Edge AI
- URL: http://arxiv.org/abs/2306.06603v1
- Date: Sun, 11 Jun 2023 06:40:51 GMT
- Title: Task-Oriented Integrated Sensing, Computation and Communication for
Wireless Edge AI
- Authors: Hong Xing, Guangxu Zhu, Dongzhu Liu, Haifeng Wen, Kaibin Huang, and
Kaishun Wu
- Abstract summary: Edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge.
Recently, convergence of wireless sensing, computation and communication (SC$2$) for specific edge AI tasks, has aroused paradigm shift.
It is paramount importance to advance fully integrated sensing, computation and communication (I SCC) to achieve ultra-reliable and low-latency edge intelligence acquisition.
- Score: 46.61358701676358
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the advent of emerging IoT applications such as autonomous driving,
digital-twin and metaverse etc. featuring massive data sensing, analyzing and
inference as well critical latency in beyond 5G (B5G) networks, edge artificial
intelligence (AI) has been proposed to provide high-performance computation of
a conventional cloud down to the network edge. Recently, convergence of
wireless sensing, computation and communication (SC${}^2$) for specific edge AI
tasks, has aroused paradigm shift by enabling (partial) sharing of the
radio-frequency (RF) transceivers and information processing pipelines among
these three fundamental functionalities of IoT. However, most existing design
frameworks separate these designs incurring unnecessary signaling overhead and
waste of energy, and it is therefore of paramount importance to advance fully
integrated sensing, computation and communication (ISCC) to achieve
ultra-reliable and low-latency edge intelligence acquisition. In this article,
we provide an overview of principles of enabling ISCC technologies followed by
two concrete use cases of edge AI tasks demonstrating the advantage of
task-oriented ISCC, and pointed out some practical challenges in edge AI design
with advanced ISCC solutions.
Related papers
- Green Edge AI: A Contemporary Survey [49.47249665895926]
We present a contemporary survey on green edge AI.
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of deep learning (DL)
We explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication [53.78269720999609]
Generative AI applications have been recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC)
It is challenging to provide qualified AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources.
We propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework.
arXiv Detail & Related papers (2023-10-26T18:05:22Z) - Integrated Sensing-Communication-Computation for Edge Artificial Intelligence [41.611639821262415]
Integrated sensing-communication-computation (I SCC) is of paramount significance for improving resource utilization.
This article presents various kinds of I SCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers.
arXiv Detail & Related papers (2023-06-01T21:35:20Z) - The Internet of Senses: Building on Semantic Communications and Edge
Intelligence [67.75406096878321]
The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors'
We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
arXiv Detail & Related papers (2022-12-21T03:37:38Z) - Task-Oriented Sensing, Computation, and Communication Integration for
Multi-Device Edge AI [108.08079323459822]
This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC)
We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
arXiv Detail & Related papers (2022-07-03T06:57:07Z) - Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G
Latency Sensitive Services [10.718353079920007]
This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management.
The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
arXiv Detail & Related papers (2021-03-18T14:18:34Z) - Towards AIOps in Edge Computing Environments [60.27785717687999]
This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments.
It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
arXiv Detail & Related papers (2021-02-12T09:33:00Z) - Communication-Efficient Edge AI Inference Over Wireless Networks [33.1306043471745]
We present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services.
This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference.
arXiv Detail & Related papers (2020-04-28T08:04:06Z) - 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.