An Intent-Driven Orchestration of Cognitive Autonomous Networks for RAN management

Banerjee, Anubhab; Mwanje, Stephen S.; Carle, Georg

doi:10.23919/cnsm52442.2021.9615505

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…The job of MNO becomes easier if there is a layer of abstraction between MNO and RAN where MNO can specify KPI related targets as intents and the layer of abstraction generates the appropriate actions to be executed in the network. In [3] we proposed the design of such a layer of abstraction which consists of three components: (i) CAN, (ii) intent specifi-…”

Section: Background: Intent-driven Can Orchestrationmentioning

confidence: 99%

“…If the intent from MNO is in the form of sentences in English language, it is necessary to parse the content of the intent and convert the intent to a predefined structure which is understandable by different network components. This intent, which is machine legible by different network components, is called a formal intent [3]. Main functionality of ISP is to convert an intent from MNO into a formal intent.…”

Section: B Ispmentioning

confidence: 99%

“…For example, an intent like "switch off cell X" is beyond the operational capability of CAN. If II finds that an intent is out of scope, depending on the implementation design it may forward the intent to some other entities or back to MNO [3].…”

Section: Idnafomentioning

confidence: 99%

“…IDM also asks the CFs to send the necessary configurations to the Controller which are required to execute the KPI related actions and IDM asks the Controller to change the configurations proposed by the CFs. In case of a conflict, the Controller resolves it using NSWF [3].…”

Section: End-to-end Workflowmentioning

confidence: 99%

“…Earlier in [3] we provided an intent based interface for MNO to use CAN for managing RAN control parameters. Although the design serves its purpose, the major drawback with the design is that it cannot detect contradictions within an intent.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Contradiction Management in Intent-driven Cognitive Autonomous RAN

Banerjee

Mwanje

Carle

2022

2022 IFIP Networking Conference (IFIP Networking)

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Section: Background: Intent-driven Can Orchestrationmentioning

confidence: 99%

Section: B Ispmentioning

confidence: 99%

Section: Idnafomentioning

confidence: 99%

Section: End-to-end Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Contradiction Management in Intent-driven Cognitive Autonomous RAN

Banerjee

Mwanje

Carle

2022

2022 IFIP Networking Conference (IFIP Networking)

Self Cite

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A Brief Survey and Implementation on AI for Intent-Driven Network

Huang

Yang

Kou

et al. 2022

2022 27th Asia Pacific Conference on Communications (APCC)

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Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

Banerjee,

Carle

2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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Over the past few years, network traffic has grown exponentially with the rise in mobile communication network users and services. This, in turn, increased the operational complexity and hiked the capital and operational expenses of mobile network operations. Present mobile networks are highly complex systems comprising automation mechanisms that leverage numerous technologies. These technologies and automation tools have been developed to deal with the configuration, optimization, and troubleshooting of mobile network operations, facilitating an efficient network management. Self-Organizing Networks (SONs) are an example of one such technology that aims to decrease human intervention by automating network management tasks. Although SON is used widely as an automation tool, it has some shortcomings; particularly, maintenance and upgrade of a rule-based management system like SON are challenging. To address these shortcomings, research is aimed at incorporating machine learning (ML) capabilities in SON and introduced a new management paradigm called cognitive autonomous networks (CANs). Like SON, CAN consists of multiple network automation functions called cognitive functions (CFs). A CF is an intelligent network function that is responsible for managing specific optimization related tasks. Throughout this thesis, we use the following CFs that are relevant for the use cases covered in this thesis: mobility load balancing (MLB), mobility robustness optimization (MRO), coverage and capacity optimization (CCO), and energy savings (ES). These functions have already been standardized by both the network operator community as well as the standardization bodies for SON use cases.These CFs operate on the same radio network in parallel and learn to optimize the same set of radio network parameters. Since they work independently but simultaneously try to adjust the same set of parameters, conflicts are likely to happen among them. These conflicts need to be resolved to keep the network stable and the network operations uninterrupted. The conflicts can be resolved by introducing some coordination among the CFs. In this thesis, we evaluate different paradigms of coordination for CAN and establish that centralized coordination is the most beneficial for CAN. In the first part of the thesis, we propose solutions for centralized coordination for conflict resolution among the CFs. These solutions are based on the Nash Social Welfare Function (NSWF) and a more advanced version of NSWF, the Fisher Market Model, combined with Eisenberg-Gale optimization. Our proposed solutions are designed to serve the combined interest of all the CFs while resolving a conflict. However, when compared between these two approaches, we find that the second one provides a significant improvement over the first one regarding overall network performance.Keeping with the current trend of making the network management system more open, flexible, and agile, in this thesis, we consider an open, multi-vendor CAN where different Finally, I sincerely thank Saya...

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An Intent-Driven Orchestration of Cognitive Autonomous Networks for RAN management

Cited by 9 publications

References 7 publications

Contradiction Management in Intent-driven Cognitive Autonomous RAN

Contradiction Management in Intent-driven Cognitive Autonomous RAN

A Brief Survey and Implementation on AI for Intent-Driven Network

Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

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