Toward Native Artificial Intelligence in 6G Networks: System Design, Architectures, and Paradigms

Wu, Jianjun; Li, Rongpeng; An, Xueli; Peng, Chenghui; Liu, Zhe; Crowcroft, Jon; Zhang, Honggang

doi:10.48550/arxiv.2103.02823

Cited by 5 publications

(12 citation statements)

References 5 publications

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“…• In-network computing: With the deployment of programmable network techniques, in-network computing can enable the share of CF within the network through the open programmable heterogeneous CF resources, which can accelerate the data process, reduce the response latency, and simplify the deployment process of applications. 2) AI-native network: With the impressive performance of AI in the past few years, treating AI as a cornerstone to enable "intelligence inclusion" has become the consensus for 6G networks [22]- [24]. Faced with the proliferation of intelligent devices and applications, such as the smartphones, tablets, and smart home, the 6G network is envisioned to accomplish the following transformations [15] to enhance data security and privacy, as well as to improve the overall performance and efficiency of the network [25], [26].…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

“…To this end, the data processing is expected to be moving from the cloud center to the edge to reduce the need for massive data exchange [27]. • From cloud AI to distributed AI: As the powerful heterogeneous CF progressively extends to the edge, the 6G network is envisioned to feature highly distributed AI [22]. In contrast to the traditional cloud AI, which mainly acts as an optimization tool or an over-the-top additional application, the distributed AI can be integrated into the future network design to enable more users to have the same capability to access intelligent services anytime and anywhere [15], [22].…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

“…• From cloud AI to distributed AI: As the powerful heterogeneous CF progressively extends to the edge, the 6G network is envisioned to feature highly distributed AI [22]. In contrast to the traditional cloud AI, which mainly acts as an optimization tool or an over-the-top additional application, the distributed AI can be integrated into the future network design to enable more users to have the same capability to access intelligent services anytime and anywhere [15], [22]. Implementing AI in a distributed manner on the user side can further improve learning efficiency and address critical security issues for users [22].…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

“…In contrast to the traditional cloud AI, which mainly acts as an optimization tool or an over-the-top additional application, the distributed AI can be integrated into the future network design to enable more users to have the same capability to access intelligent services anytime and anywhere [15], [22]. Implementing AI in a distributed manner on the user side can further improve learning efficiency and address critical security issues for users [22]. Meanwhile, the coordination of the intelligence distributed in cloud, edge, devices, and network nodes can achieve the adaptation of resources such as spectrum, computing, and storage without human intervention, which can further enhance the overall network efficiency [15].…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

“…In this regard, integrating the philosophy of SemCom, i.e., understanding the data before transmission and sending only the information that is considered important, into the intelligent agents can further enhance network efficiency by reducing communication overheads. • From connection-oriented communication to taskoriented communication: The most typical service in conventional communication is establishing a connection between two specific terminals, wherein it is easy to tell an explicit pair of source and destination terminals according to the content they intend to communicate [22]. However, with the advent of intelligent applications based on distributed AI, such as full autonomous driving based on predictive quality of service (QoS) [28], connection-oriented communication will be transformed into task-oriented communication.…”

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

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Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

Section: A 6g Visions and Key Enablersmentioning

confidence: 99%

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Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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An Autonomous Deployment Mechanism for AI Security Services

Wang,

Zhou,

et al. 2024

IEEE Access

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Future network architectures are expected to be autonomous, intelligent, and service-based, posing new security challenges. To address these challenges, the Artificial Intelligence (AI) security service emerges as a promising solution. However, the complex service configurations and performance guarantees hinder the autonomous deployment of the AI security service. This paper proposes an autonomous deployment mechanism in Software-Defined Networking/Network Function Virtualization (SDN/NFV) enabled networks. First, our mechanism introduces user and decision planes on top of the control plane, enabling hierarchical intent expression and translation from user security intent to security policies. Then, we analyze the embedding problem of the AI-based Security Function Chain (AISFC) during security policy generation. We formulate the AISFC embedding problem as an Integer Linear Programming (ILP) task to minimize the total response delay. By decomposing it into AISF placement and routing, we design a heuristic algorithm with polynomial time complexity. Finally, we validate the proposed mechanism through a prototype system and numerical simulations, demonstrating its ability to autonomously translate, implement, and guarantee the user security intent. Comparative analysis shows that our approach considering the relationship between available computing resources and delay achieves smaller response delays than the baseline. Furthermore, our algorithm achieves a gap from optimality approximately 28.57% smaller than the greedy algorithm and supports networks that are 4.34 times larger in scale than the exact solution within a 2-second execution time.

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Distributed Intelligence in Wireless Networks

Liu

et al. 2023

IEEE Open J. Commun. Soc.

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The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of the large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Besides, the deployment of AI techniques to redesign end-to-end communication is attracting attention to improve communication performance. Therefore, the interaction of AI and wireless communications generates a new concept, named native AI wireless networks. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native AI wireless networks, with a focus on the design of distributed learning architectures for heterogeneous networks, on AI-enabled edge computing, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to state-of-the-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify potential future opportunities.

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Toward Native Artificial Intelligence in 6G Networks: System Design, Architectures, and Paradigms

Cited by 5 publications

References 5 publications

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

An Autonomous Deployment Mechanism for AI Security Services

Distributed Intelligence in Wireless Networks

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