Toward Autonomous Digital Populations for Communication-Sensing-Computation Ecosystem

TL;DR

This paper proposes a nature-inspired, digital twin-based framework for autonomous, adaptive communication networks that integrate sensing, computation, and communication through digital populations and ecosystems.

Contribution

It introduces a novel architectural framework leveraging digital twin technology and digital populations to enable autonomous, evolvable communication networks.

Findings

Framework supports dynamic coordination and distributed decision-making.

Enables continuous adaptation and evolution of network functions.

Lays theoretical foundation for next-generation autonomous networks.

Abstract

Future communication networks are expected to achieve deep integration of communication, sensing, and computation, forming a tightly coupled and autonomously operating infrastructure system. However, current reliance on centralized control, static design, and human intervention continues to constrain the multidimensional evolution of network functions and applications, limiting adaptability and resilience in large-scale, layered, and complex environments. To address these challenges, this paper proposes a nature-inspired architectural framework that leverages digital twin technology to organize connected devices at the edge into functional digital populations, while enabling the emergence of an evolvable digital ecosystem through multi-population integration at the cloud. We believe that this framework, which combines engineering methodologies with sociotechnical insights, lays the theoretical foundation for building next-generation communication networks with dynamic coordination, distributed decision-making, continuous adaptation, and evolutionary capabilities.