© 2021 Nokia1Public

Towards Cognitive Autonomous Networks (CAN): 5G and Beyond

Henning Sanneck, Stephen Mwanje, Janne Ali-Tolppa, Marton Kajo & Team

Network Automation Research,Nokia StandardsMunich, Germany

© 2021 Nokia2

Modelling Cognitive Decision MakingA Perception-Reasoning pipeline

Trigger

action(s)

reflex actions subconscious actions conscious actionsExternal closed loop

Memory

Fetch(F), Read labels(R), (re)label(L) (if needed

depending on perception), and (re)store (S)

Sense Conceptualize

Contextualize

Organize

Infer

Reasoning

Perception

Public

© 2021 Nokia3

The Need for Cognitive Autonomy in Communication Networks Definitions

Tak

e one

or

more

act

ion(s

)

Diagnose events

Contextualize events/data

Anticipate standalone events

Anticipate correlated events

Correlate events/data

Detect a network event

Preset time/sequence trigger

Auto

no

mous

Cogn

itiv

e-A

uto

nom

ous

Cogn

itio

n i

ncr

ease

s

Self-Organizing – Achieve steady state without external control

o automates the selection and execution of actions

o interprets events & context to determine the cause-effect relations.

Autonomous - able to act on its own, without dictation or rules from anyone else

o not necessarily able to reason based on its environment or even smart enough to make the best static decisions

Cognitive - able to reason and formulate recommendations for subsequent behaviour

o may however require the operator’s approval

Public

© 2021 Nokia4

Static model vs. learning from dataCognition in Communication Networks

Classic AI: Model is available

o Static – fixed rule set on discrete or continuous valued logic

o Adaptive to deployment – by probabilities

Learning: Happens if performance at task T as measured by performance measure P, improves with experience E [1]

o Parameterise a model (θ) with loss function J → P=J(θ)

o Iteratively update model with experience E from data

[1] adapted from Tom Mitchell (1997))

𝜃:= 𝜃 − 𝛼𝜕

𝜕𝜃𝐽(𝜃)

Expanded & New interfaces for standardization – data & model transfer

Problem

inputsModel

Human develops

model

Process

Classic AI

std std

Problem

inputsModel

Learn patterns on data

structure or that match

data to labels

Fit inputs to patterns

Process

Build

Model

Training

Data

Machine Learning

std

std std

std

Public

© 2021 Nokia5

Autonomy

Manual Assisted Partially automated Automated Partially

autonomous

AutonomousC

og

nit

ion

Machine: None

Human execution &

supervision

Machine: assisted

execution &

supervision

Human: partial

execution

Machine: partial

execution;

Human: supervision via

policy

Machine: execution;

Human: supervision

via policy

Machine: execution &

partial supervision

Human: policy &

intent

Machine: execution &

supervision;

Human: intent-only

+ Anticipate

correlated

events

+ Anticipate

individual

events

+ Context-

ualize

Machine2human

visualization

+ Machine profiling

+Automatic re-act

+ Machine

automatic re-action

selection

+ Machine learning

of new policies

+ Machine

reasoning

+ General learning;

+Trustworthiness

+ Diagnose

eventsMachine2human

selective exposure

+ Machine mapping to

causes (rules)

+Automatic re-act

+ Human labelling

of causes identified

by machine

+ Model-free

(Reinf- Learning)

+ Transfer learning

+ Machine

explanation

+ Correlate

eventsHuman correlation Machine correlation +Automatic re-action (n.a. – due to limited

- scalability of automation- feasibility of machine supervision

in a system with low cognitive capability)Detect an

event Human detection Machine detection +Automatic re-action

Cognition vs. AutonomyTaxonomy

Human diagnosis

Human experience+ Machine prediction

+ Automatic pro-action

+ Machine automatic

pro-action selection

+ Machine

prediction of new

policies

+ Machine

reasoning

PLANARCaseStudy

© 2021 Nokia6

A live testbed created in the 5G MoNArch projectdemonstrating 5G slicing at the Hamburg Harbor:

• Three slices

• eMBB: Local applications in the harbor

• URLLC: Traffic light control

• IoT: Emission sensor readings from barges

• Data collection

• Slice-specific BTS KPIs: PRB usage, throughput, latency etc.

• UE measurements from up to three ships including position (by GPS), RSRP, RSRQ, ping etc.

• Collected for 6 months every 5 seconds~3M records

The 5G network slicing testbed at Hamburg HarborPredictive Location-Aware Network Automation for Radio

Local Applications(HPA, Veddeler Damm 18)

Heinrich HertzTower

Sensor measurements

on barges

Public

PLANARCaseStudy

© 2021 Nokia7

Problem statementPredictive Location-Aware Network Automation for Radio

• IoT requires high reliability

• In certain areas of the testbed, coverage and mobility issues are observed in the IoT slice

• Shadowing effects and/or

• Long distances from the base station

• Reliable service must be guaranteed, but without overprovisioning of resources or compromising the performance of the other slices

RSRP of USRP1 [dBm]

Ping [ms]

LOS

BTS

Non-LOS

Public

© 2021 Nokia8

Prediction of Mobility and QoS/RSRPPredictive Location-Aware Network Automation for Radio

Radio Propagation Map:

• Created based on UE measurements (reported GPS position, RSRP)

• Using a FNN

Mobility Pattern Prediction (MPP):

• Positions reported by the barges

• Prediction of barge movement using a convolutional neural network Combining the mobility prediction

with the coverage model, of 62200 sequences in a validation set, we were able to predict up to 90% of the low-RSRP events and RLFs 40 seconds ahead

RSRP, serving cell: Cell1

RSRP, serving cell: Cell2

Input sequence

Ground truth

Prediction

Public

© 2021 Nokia9

Closed-Loop Automation Evaluation with SimulationPredictive Location-Aware Network Automation for Radio

A digital twin of the testbed setup is mirrored in a simulator

• Full 3D model of the city of Hamburg and especially the harbor area

• Network topology and configuration as in the real testbed

• Traces of the movement of the real barges are collected from the testbed and imported into the simulation scenario

• The coverage issues of the real testbed can be reproduced

Public

© 2021 Nokia10

Closed-Loop Automation Evaluation with SimulationPredictive Location-Aware Network Automation for Radio

The digital twin is extended to simulate NR with beam forming

• Four beams per each of the two cells, with two antenna elements for two simultaneous beams

Public

© 2021 Nokia11

Predictive Location-Aware Network Automation for Radio Demo @ IEEE NOMS 2020

Actors

ML model inference

PLANAR

Management

Hamburg Testbed

Scenario

Recorded

movement

Trx Power

Beam Adaptation

System-

Level RAN

Simulator

pre

dictio

ns

Data Collector Deployment

Public

https://www.youtube.com/watch?v=nMdBbLv2G98

Mobility Prediction QoS prediction

© 2021 Nokia1212

Predictive Location-Aware Network Automation for Radio (PLANAR)Preventive Closed-Loop Optimization

RS

RP

[DB

]

-100

-120

-140

-160

Thro

ughput

[kbits/s

ec] 150

100

50

0Time

Measurement

Prediction

(40 sec. ahead)

Predictied RLF

Corrective action deployed

40 s

RLFHO

Public

QoS and RLFs can be predicted and prevented by:

• Optimizing the transmission power

• Beam forming

• Activate or de-active beams

• Tilting of individual beams

• Prediction allows higher thresholds to be used in the optimization algorithms

• Minimized overprovisioning of resources

• Minimized compromises between different network slicesTX Power change

Demo GUI

Ce

llID

Beam tilt

© 2021 Nokia13

Project (kick-project.de)

Application of AI methods in a highly dynamic wireless network and flexibly reconfigurable factory environment for monitoring and controlling communication and production

Artificial Intelligence for Campus Communication

Stuttgart Munich

Public

© 2021 Nokia14

SECURITY

ARCHITECTURE

ETHICS

SUSTAINABILITY

AI

GOVERNANCE INNER AI

EXECUTIONACTION

INTER AI

AI in Cognitive Autonomous NetworksEnabling areas

Public

PLANARCaseStudy

PLANARCaseStudy

PLANARCaseStudy

© 2021 Nokia15

Towards Cognitive Autonomous Networks

Rule-based management functions,Software Management

LTE / early 5GSelf Organizing

Network

Learning management functions, Software Management (DevOps)

5G EvolutionCognitive

Network Management

Management of training + Comprehension (C)

Towards 6GCognitiveNetwork

(C)NF: (Cognitive) Network Functions

Learning network functions(embedded management)

Human2machine i/f

CNF

Management of training

CNF CNF

C

CNM CNMCNM

Rule-based / data-driven network functions

NF+ NF+ NF+

SW Mgmt. (DevOps)

Human2machine i/f

SON SON SON

SON operation

Rule-based network functions

NF NF NF

Human2machine i/f

SW Mgmt.

5G and Beyond (RAN)

Public

© 2021 Nokia16

Towards Cognitive Autonomous Networks5G and Beyond (RAN)

CNF

Management of training +

Comprehension

LTE / early 5GSelf Organizing

Network

5G EvolutionCognitive

Network Management

Towards 6GCognitive Autonomous

Network

SON

CNM

SON

CNM

OAM connectivity / interface setup

Resource ID allocation (beam/cell ID/RS)

Neighbour relationship setup (ANR)

Mobility mgmt. / robustness / load balancing

Anomaly Detection → Diagnosis → Healing

Mobility load balancing / traffic steering

VNF Scaling down/up; Placement

Coverage and Capacity Optimization

Resource ID allocation (beam/cell ID/RS)

Mobility robustness (MRO)

Sleeping Cell / Outage Detection

Mobility load balancing / traffic steering

VNF Scaling in/out, down/up; Placement

Basic Coverage and Capacity Optimization

OAM connectivity / interface setup

Neighbour relationship setup (ANR)

OAM connectivity / interface setup

VNF Scaling in/out

RRM & Coverage and Capacity Optimization

Traffic steering

Mobility robustness (MRO)

VNF Scaling in/out, down/up; Placement

SON

Anomaly Detection → Diagnosis → Healing

Public

PLANARCaseStudy

© 2021 Nokia17

Towards Cognitive Autonomous NetworksConclusions

• Cognition & Autonomy are crucial, separable properties• Understand system concepts and contexts to enable decision making at machine level• Actions can be taken at any step in the cognitive process

• AI Enablers: Data → Execution → Action; inter-AI & Governance• “Inner AI”: telco- / NM-specific choice and adaptation of AI Algorithms• AI requires expanded (training data) and new (model management) standardized interfaces

• LTE/early 5G: SON → 5.5G: Cognitive NM (CNM) → 6G: Cognitive Autonomous Network (CAN)• Cognition & Autonomy penetrate the u- and c-plane

• AI is the technology area to enable this• m-plane takes a new role (managing the AI on the u- and c-plane)

• Process (integration / replacement of legacy functions)• Some SON and CNM functions remain

→ Performance improvement of existing network (management) functions & enabling of new functions → Simplification from operator perspective (shift from “execution” to “supervision”)

Public

© 2021 Nokia18

Towards Cognitive Autonomous NetworksNetwork Management Automation for 5G and beyond

• Published October 2020

https://www.wiley.com/en-us/Towards+Cognitive+Autonomous+Networks+%3A+Network+Management+Automation+for+5G+and+Beyond-p-9781119586388

Book structure and outline

Background Core argumentation Solutions/Application Open challenges

1. Introduction2. Network Evolution

4. Modeling Cognition

3. SON in pre-5G Networks

5. Classical AI for reasoning

7. Cognitive Auto-Configuration

8. Cognitive Autonomy in Optimization

9. Cognitive Self-Healing

11. System Challenges of CANs

12. Towards realizing CANs

6. ML for cognitive decision making

10. Cognitive Self-Operation

© 2021 Nokia19

Towards Cognitive Autonomous NetworksRecent journal / conference publications & tutorials

• S. Mwanje, M. Kajo, J. Ali-Tolppa, Environment Modeling and Abstraction of Network States using DeepClustering, IEEE Network special issue "AI for mobile networks“ 2020

• M. Kajo, B. Schultz, Deep clustering of mobile network data with sparse autoencoders, IEEE NOMS 2020• M.L. Mari-Altozano, S. Mwanje, S. Luna-Ramirez, M. Toril, H. Sanneck, C. Gijon, A Service-centric Q-learning

Algorithm for Mobility Robustness Optimization in LTE, IEEE TNSM, 2021.• A. Masri, T. Veijalainen, H. Martikainen, J. Ali-Tolppa, S. Mwanje, M. Kajo, Machine Learning Based Predictive

Handover, IEEE IM 2021• A. Banerjee, S. Mwanje, G. Carle, Optimal configuration determination in cognitive autonomus networks, IEEE

IM 2021• A. Banerjee, S. Mwanje, G. Carle, RAN Cognitive Controller, 2nd KuVS Fachgespräch on Machine Learning and

Networking, 2020 • A. Banerjee, S. Mwanje, G. Carle, Game theoretic Conflict Resolution Mechanism for Cognitive Autonomous

Networks, IEEE SPECTS 2020• P. Kalmbach, P. Diederich, W. Kellerer, R. Pries, A. Blenk, sfc2cpu: Operating a Service Function Chain Platform

with Multi-Agent Reinforcement Learning, IEEE IM 2021• S. Mwanje, Towards Cognitive Autonomous Networks, Tutorial, IEEE NoF 2020• S. Mwanje, Towards Cognitive Autonomous Networks, Tutorial, IEEE CNSM 2020

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