Jun
15
Autonomous and evolutive optimization in networked AI represents a transformative paradigm for Signal Processing and Artificial Intelligence (AI) communities. By integrating traditional knowledge-based adaptive signal processing techniques and data-centric deep-neural network models, new developments in the area can dynamically acquire high-quality data in the continuous inferences of networked AI models, and optimizes every individual model by adaptively generating corresponding rewards and pseudo-labels online. These mechanisms not only mimic how human organizations evolve and learn beyond any individual machine-leaning paradigms, but also can unify supervised and reinforcement learning in the networking systems of AI, by the adaptive signal processing.
By leveraging dynamic interactions among multi-agent systems, it enables autonomous self-optimization and evolution of networked AI, ensuring robust performance in time-varying environments without human interventions, and with much scalable complexity. The interdisciplinary nature of adaptive networked AI extends its influence on Signal Processing, Internet of Things, Communication, and Computer Societies, as well as promoting various applications such as industry-specific large language models, scene-adaptive auto-driving systems, real-time 3D reconstruction, etc. This special issue aims to consolidate and expand the foundational principles of the adaptive and online optimization for networked AI models, and foster its advancements in intelligent signal processing systems. An outline of topics on which we plan to solicit submissions is as follows:
- Foundations and principles of signal processing in networking systems of AI
- Mathematical underpinnings of networked AI optimization
- End-cloud collaborative large language models with evolutive optimization
- Coordinated sensing and control processing in autonomous multi-agent AI systems
- Multimodal and adaptive signal processing with networked AI
- Networked AI for cognitive communications and networks
- Online model-drift detection and compensation mechanisms in networked AI
- Networked AI enhanced signal processing systems in non-stationary environments
- Practices of autonomous and evolutive learning for networked AI systems
Prospective authors should follow the instructions given on the IEEE JSTSP webpages: https://signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing and submit manuscripts at: https://mc.manuscriptcentral.com/sps-ieee.
Dates
- Submissions Due: 15 June 2026
- First Review Due: 14 August 2026
- Revised Manuscript Due: 1 October 2026
- Second Review Due: 2 November 2026
- Final Decision: 20 November 2026
- Publication: January 2027
Guest Editors
- Liang Song, Fudan University, China, songl@fudan.edu.cn (Lead GE)
- Jiangchuan Liu, Simon Fraser University, Canada, jcliu@sfu.ca
- Amit Dvir, Ariel University, Israel, amitdv@g.ariel.ac.il
- Athanassios Skodras, University of Patras, Greece, skodras@upatras.gr
- Victor C.M. Leung, University of British Columbia, Canada, vleung@ece.ubc.ca
- Qi Bi, China Telecom Research Institute, China, qibi@chinatelecom.cn
