Requirements and Specifications for the Orchestration of Network Intelligence in 6G

Abstract

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.

Network Intelligence for Virtualized RAN Orchestration: The DAEMON Approach

“…More generally, both functions are basically custom logic to perform RAN optimization, told apart by the fact that they operate at different time scales. When such r/xApps are empowered by AI/ML algorithms, they match the definition of NI [10]. However, to integrate NI natively in next-generation network architectures, such as the AI/ML-based r/xApps in O-RAN, a large range of NI instances will need to interact seamlessly to perform at their best, and exchange data and information to mutually improve both their learning and decision-making processes.…”

“…The mobile network architecture proposed in the context of the DAEMON project 1 is NI-native and goes several steps beyond the current standardization trends [5], [7]- [9]: it posits a new approach for systematic integration of NI in 6G infrastructure while staying entirely aligned with emerging designs in standardization [2]. In our previous work [10], we outlined general requirements and specifications for NI design that stem from data management, diverse control timescales, and network technology characteristics; we also derived initial principles for the design of an NI Orchestration layer, which focuses on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms.…”

“…In this paper, we build on the concepts introduced in [10] and exploit DAEMON unified representation of NI algorithms to integrate NI in the context of vRAN orchestration and control problems. The latter is an appropriate and representative context for the embedding of NI solutions into a larger system because (i) they deal with high-dimensional state spaces (including, e.g., channel quality patterns or network load patterns across a potentially large number of users and base stations), and (ii) orchestration and control decisions are often coupled among a large number of parameters (for instance, radio and computing resource policies).…”

“…In these settings, finding a suitable framework that maximizes the reuse of ML models across diverse NI solutions is of paramount importance: it paves the way for a structured approach to ML assimilation in networking systems, greatly simplifying future innovations in NI for beyond-5G and 6G networks. Specifically, we present mappings between the N-MAPE-K loop proposed as the unified NI representation within DAE-MON [10] and two sample NI algorithms developed in the project: vrAIn [11] and SBP-vRAN [12]. We show that the DAEMON architecture promotes the reuse of NI components, such as monitoring ones, among heterogeneous algorithms used inside each micro-domain.…”

“…In Section III, we describe the two state-of-the-art ML-based algorithms for vRAN optimization that are considered in our study. We define their specific requirements as NI algorithms, and we map them to the unified representation of NI proposed in [10] in Section IV. Finally, Section V realizes them via a framework for data flow programming to demonstrate its generality, and Section VI discusses conclusions and future work.…”