Generative AI in mobile networks: a survey

Karapantelakis, Athanasios; Alizadeh, Pegah; Alabassi, Abdulrahman; Dey, Kaushik; Nikou, Alexandros

doi:10.1007/s12243-023-00980-9

Cited by 22 publications

(3 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiments were repeated six times to limit the randomness of the results. Each model was trained for a maximum of 15 epochs without premature termination based 3 documentation on the loss of validation. Considering the backdoor configuration, models, and repetition of experiments, all backdoored models were cross-validated k-fold (k = 5).…”

Section: Testing Deep Neural Network (Model Architectures Dnns)mentioning

confidence: 99%

See 1 more Smart Citation

A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks

Mengara

2024

IEEE Access

View full text Add to dashboard Cite

Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. This exposes deep neural network (DNN) models trained on such data to potential data poisoning attacks. In this type of assault, attackers can train the DNN model using poisoned data, potentially degrading its performance. Another type of data poisoning attack that is extremely relevant to our investigation is label flipping, in which the attacker manipulates the labels for a subset of data. It has been demonstrated that these assaults may drastically reduce system performance, even for attackers with minimal abilities. In this study, we propose a backdoor attack named "DirtyFlipping", which uses dirty label techniques, 'label-on-label', to input triggers (clapping) in the selected data patterns associated with the target class, thereby enabling a stealthy backdoor.INDEX TERMS Poisoning attacks, Backdoor attacks, Adversarial machine learning.

show abstract

Section: Testing Deep Neural Network (Model Architectures Dnns)mentioning

confidence: 99%

“…In recent years, machine learning systems have experienced exponential growth in a variety of fields [1], from facial recognition to speech synthesis to more recent generative models [2] [3]. Indeed, machine learning techniques have become a common tool in the lives of the general public, with one of the most important being automatic speech recognition.…”

mentioning

confidence: 99%

A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks

Mengara

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Given the challenges faced by DAI models in the industrial IoT paradigm, [21] suggests the adoption of GMs and reviews the state-of-the-art GMs, categorizing them based on their relevance to IIoT: anomaly detection, trust-boundary protection, network traffic prediction, and platform monitoring. Karapantelakis et al [22] offers a detailed review of GenAI's application in mobile telecommunications networks by categorizing literature based on GM types, its purpose, and the targeted mobile network component. A pivotal discussion in this study is the examination of the current state of AI integration in major telecom standardization entities.…”

mentioning

confidence: 99%

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

Celik,

Eltawil

2024

Preprint

View full text Add to dashboard Cite

As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a profound fusion with novel communication paradigms and emerging technological trends, bringing once-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML) and artificial intelligence (AI) can efficiently deal with complex problems by complementing or replacing model-based approaches. 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, autoregressive GMs, 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 1) physical layer design; 2) network optimization, organization, and management; 3) network traffic analytics; 4) cross-layer network security; and 5) localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic communications, integrated sensing and communications, THz communications, extremely large antenna arrays, near-field communications, 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. Given its depth and breadth, we are confident that this tutorial-cum-survey will serve as a pivotal reference for researchers and professionals delving into this dynamic and promising domain.

show abstract

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Xu,

Omitaomu,

Sabri

et al. 2024

Urban Info

View full text Add to dashboard Cite

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

show abstract

Generative AI in mobile networks: a survey

Cited by 22 publications

References 85 publications

A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks

A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks

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

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Contact Info

Product

Resources

About