Multi-agent reinforcement learning for intent-based service assurance in cellular networks
This addresses scalability and coordination issues in telecom network management for operators, though it is incremental as it applies MARL to a known bottleneck.
The paper tackles the problem of intent-based service assurance in cellular networks by proposing a multi-agent reinforcement learning (MARL) method to manage conflicting intents and optimize key performance indicators (KPIs) without needing a system model, achieving fulfillment of all intents when resources are sufficient and prioritization when scarce.
Recently, intent-based management has received good attention in telecom networks owing to stringent performance requirements for many of the use cases. Several approaches in the literature employ traditional closed-loop driven methods to fulfill the intents on the KPIs. However, these methods consider every closed-loop independent of each other which degrades the combined performance. Also, such existing methods are not easily scalable. Multi-agent reinforcement learning (MARL) techniques have shown significant promise in many areas in which traditional closed-loop control falls short, typically for complex coordination and conflict management among loops. In this work, we propose a method based on MARL to achieve intent-based management without the need for knowing a model of the underlying system. Moreover, when there are conflicting intents, the MARL agents can implicitly incentivize the loops to cooperate and promote trade-offs, without human interaction, by prioritizing the important KPIs. Experiments have been performed on a network emulator for optimizing KPIs of three services. Results obtained demonstrate that the proposed system performs quite well and is able to fulfill all existing intents when there are enough resources or prioritize the KPIs when resources are scarce.