Intent Profiling and Translation Through Emergent Communication
Salwa Mostafa Mohammed S. Elbamby Mohamed K. Abdel-Aziz Mehdi Bennis
To effectively express and satisfy network application requirements,
intent-based network management has emerged as a promising solution. In
intent-based methods, users and applications express their intent in a
high-level abstract language to the network. Although this abstraction
simplifies network operation, it induces many challenges to efficiently express
applications' intents and map them to different network capabilities.
Therefore, in this work, we propose an AI-based framework for intent profiling
and translation. We consider a scenario where applications interacting with the
network express their needs for network services in their domain language. The
machine-to-machine communication (i.e., between applications and the network)
is complex since it requires networks to learn how to understand the domain
languages of each application, which is neither practical nor scalable.
Instead, a framework based on emergent communication is proposed for intent
profiling, in which applications express their abstract quality-of-experience
(QoE) intents to the network through emergent communication messages.
Subsequently, the network learns how to interpret these communication messages
and map them to network capabilities (i.e., slices) to guarantee the requested
Quality-of-Service (QoS). Simulation results show that the proposed method
outperforms self-learning slicing and other baselines, and achieves a
performance close to the perfect knowledge baseline.