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TCCN SI 2024 : Generative AI for Next-Generation Networks and Communication: Challenges and Solutions


When N/A
Where N/A
Submission Deadline Jul 1, 2024
Notification Due Dec 30, 2024
Final Version Due Jan 30, 2025
Categories    computer network   next-generation network   generative ai

Call For Papers

IEEE Transactions on Cognitive Communications and Networking (TCCN)
Special Issue on
Generative AI for Next-Generation Networks and Communication: Challenges and Solutions

In the dynamic landscape of communication networks, traditional Artificial Intelligence (AI) algorithms have been instrumental, optimizing and troubleshooting functions within communication networks. Beyond this, artificial intelligence holds the potential for computers or robots to command systems, empowering network operators to strategically plan, execute, and manage services in response to evolving consumer demands. Artificial intelligence aids operators by providing precise predictions without actual implementations. It facilitates process automation, enhancing efficiency while minimizing operating costs. However, as we approach the Sixth Generation of Mobile Networks (6G) with heightened demands for connectivity, capacity, data rates, latency, mobility, and reliability, the limitations of these traditional AI models become evident.

Recent breakthroughs in Generative Artificial Intelligence (GAI), such as transformer-based models like Generative Pretrained Transformers (GPTs), signify a paradigm shift in AI. GPT-3.5 and GPT-4 showcase the ability to support customization and data generation, introducing a new dimension to communication networks with dynamic adaptation. GAI's applications span text summarizers, language translators, chatbots, and tools for upskilling. GAI's impact on content creation is transformative, promising enhanced efficiency and personalized experiences in various fields. Integrating GAI into next-generation networks and communication redefines interactions, communication, and information access. GAI's lifelike speech and language generation elevate virtual assistants and chatbots. GAI revolutionizes network performance through data analysis, learning patterns, and predicting traffic dynamics, thereby enhancing resource allocation efficiency and reducing latency. This transformative approach ensures elevated quality of service, increased network capacity, and improved user experiences. GAI's applications empower intelligent decision-making in network planning, optimize infrastructure deployment, and contribute to context-aware services, dynamically adapting to users' needs. As GAI becomes integral, it catalyzes innovation, promising heightened efficiency, adaptability, and personalized user experiences in the evolving communication landscape.

Despite the transformative potential of GAI in next-generation networks and communication, seamless integration faces specific challenges. One significant hurdle is the optimization of GAI algorithms to handle large-scale data processing and communication tasks efficiently. Another challenge lies in ensuring the adaptability of GAI systems to dynamic network environments, addressing issues of scalability and real-time responsiveness. Addressing these challenges is pivotal for unlocking the full potential of GAI in shaping the future of intelligent communication networks. Therefore, this special issue aims to offer a platform for researchers from both academia and industry to publish recent research findings and to discuss opportunities, challenges, and solutions related to GAI. In particular, this Special Issue solicits original research papers about state-of-the-art approaches, methodologies, and technologies enabling efficient and practical GAI towards the realization of next-generation networks and communication. Potential topics of interest include but are not limited to the following:

GAI-based Resource Management for Next-Generation Networks and Communication
GAI-assisted Network Planning and Deploying for Next-Generation Networks
GAI-enable Mobile Computation Offloading over Next-Generation Networks and Communication
GAI-based Semantic Communication for Next-Generation Networks and Communication
GAI for Digital Twining in Next-Generation Networks and Communication
GAI-based Distributed Learning Communication for Next-Generation Networks and Communication
GAI-enable Security and Privacy Provisioning in Next-Generation Networks and Communication
GAI-based Content and Model Cache Management for Next-Generation Networks and Communication
GAI-empowered Applications for Next-Generation Networks and Communication
Quality of Service (QoS) Improvement of GAI in Next-Generation Networks and Communication
GAI-based Optimization and Acceleration Techniques for AIGC services over Next-Generation Networks
Large Model Frameworks and Techniques for GAI for Optimization
Comprehensive Performance Evaluation of GAI in Next-Generation Networks and Communication
Experimental Prototyping and Testbeds of GAI over Next-Generation Networks and Communication

Submission Guidelines
Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Cognitive Communications and Networking guidelines. Note that the page limit is the same as that of regular papers. Please submit your papers through the online system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail directly to the Guest Editors.

Important Dates
Manuscript Submission: 1 July 2024
First Review Round: 1 October 2024
Revision Papers Due: 15 November 2024
Acceptance Notification: 30 December 2024
Final Manuscript Due: 30 January 2025
Publication: 2025

Guest Editors
Xiaofei Wang
Tianjin University, China

Dusit (Tao) Niyato
Nanyang Technological University, Singapore

Jiawen Kang
Guangdong University of Technology, China

Christos Verikoukis
University of Patras, Greece

Zehui Xiong
Singapore University of Technology and Design, Singapore

Elisa Bertino
Purdue University, USA

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