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cognitive-software

Ericsson
Performance
Optimizers
Solution sheet

2Ericsson Performance Optimizers

Network optimizationtransformed with AI
AI-based network performance diagnostics analyzes communications service
providers’ Radio Access Network (RAN) to resolve network issues efficiently
and provide specific parameter change recommendations. Various advanced AI
techniques enable fast and accurate optimization of the end-user performance.
Traditionally, network performancemonitoring and optimization is handled
by a team of engineers supported byrule-based systems, able to cope withlimited areas of the network (typicallyareas with the worst-performing cells).
However, with the rapid introductionof new technologies like 5G, mobile
networks are growing and becoming
more complex. With this speed of change,performance gains become more difficult
to achieve, so optimization efforts must

evolve into broader and more agile
solutions that reach beyond humancapabilities.
Introducing Ericsson Performance
Optimizers, part of Ericsson Cognitive
Software portfolio, the first telcoindustry, field-proven suite of AI-powered
applications to use digital twin
technology and deep reinforcement
learning (RL), Ericsson Performance
Optimizers provide mobile network
optimization recommendations toproactively resolve performance issues.
The solution also accounts for theinvisible changes in the network caused
by each addition to the environment, suchas new applications, city growth, newsites or user behavior.
To ensure the coverage of automatedoptimization across mobile networks
constantly increases, Ericsson will beperiodically releasing new Performance
Optimizers that target different issues.

Accelerating AI adoption with Cognitive Software
Realizing the value of AI at scale requires overcoming some major barriers.
Trust, Flexibility and Agility are among the most challenging, and Cognitive
Software can help address these.

TrustFlexibility

Agility

Explainable AI embedded in theapplications increases transparency
in AI-driven insights and automations.

Tailored AI models and user experience.
Globally trained AI algorithms can belocally re-trained when it is convenient
to prevent model drifting and adapt
better to every network. Application
workflows and user interfaces can becustomized for seamless operational
integration

Cloud-native architecture that enables
highest flexibility for efficient AI-driven
operations. Scale up and down as
required, and facilitate integration into
CI/CD pipelines for in-service software
upgrades with 100% uptime

Flexible offering
Every automation journey is different, and every operator needs
to align this with other strategic choices in the area of software
infrastructure, management platforms, or the overarching technology
evolution.
To accommodate this diverse range of scenarios, Ericsson provides
Performance Diagnostics as a dual software solution, supporting
both standalone deployments and rApps, together with a diverse setof infrastructure options. Ericsson Performance Diagnostics can bedeployed in either dedicated hardware, private cloud, public cloud, orEricsson premises in a software as a service mode.
This flexible offering allows every operator to decide when and how totake steps forward in their transformation at their own pace and with
their own priorities.

Flexible deployment models for
Ericsson Performance Optimizers

Performance Optimizers

Performance Optimizers rApp

IaaS/Caas

Open RAN SMO

3Ericsson Performance Optimizers

Figure 1: Ericsson Performance Optimizers solution flow

Issue optimizers can operate network wide on detected issues and RCA checks.
General optimizers in areas impacted by changing trafﬁc conditions as network anduser patterns evolve.
Actionable changes

Performance Diagnostics
Networkmanagement
and physicaldata

Performance Optimizers
Optimization recommendation

Issue
classiﬁcation

Root cause
analysis70+ issues
15min1
1M cells

200+ reasons
60min1
1M cells

Solution/execution

Mobility Optimizer

Open APIIssue
optimizer
Graph Neural Network

UL Interference Optimizer
GUIGeneral
optimizer
Reinforcement Learning

RET Optimizer
ENM/OSS
scriptPower Optimizer
Digital Twin

NETWORK1

Manual execution

Actual execution time, North America

Figure 1 illustrates the high-levelarchitecture of the solution and introduces
the two different approaches: issue
optimizers and general optimizers.
Firstly, the data is collected from multi–
vendor network management systems
and translated into a normalized format
as the AI algorithm is vendor-agonistic bydesign. After data ingestion, AI analyzes
over 160 network key performanceindicators (KPIs) per cell, across all cells
in the network, processing millions ofvariables in minutes. AI identifies cellswith anomalies and clusters those withsimilar issues, for example, coverage,handover, and external interference

among 70+ different issue classes.
Once issues are classified, analyzed,
and the root cause is identified by
Ericsson Performance Diagnostics,
issue optimizers then accurately predict
network performance improvements
and provide one-shot optimization
recommendations for targeted cells.
As traffic patterns evolve with new
applications, city development, or user
behavior, network performance may
degrade. This is where general optimizers
step in, to proactively optimize network
performance and keep up with thesechanges. General optimizers leverage
deep reinforcement learning (RL) and alsouse digital twin technology.
Ericsson Performance Optimizers benefit
strongly from advanced AI techniques.
The provided key values – automation,scalability, speed, accuracy, andconsistency in network optimization –enable service providers to maintainand improve their network performanceat a higher level. Examples of tangible
improvements introduced by Performance
Optimizers are shown in Figure 2 (allvalues are from live deployments andproofs-of-concept).

Figure 2: Ericsson Performance Optimizers’ customer case results
Issue optimizers

26%

General optimizers

x40 fastercompared tothe traditional
approach1

12%
Faster downlink
throughput2Congestion value
reduced to almost
zero while trafficslightly increased2

50% 42%

3%

30%

Rise in uplink traffic
data3

Reduction in base stationenergy consumption4

Faster uplinkthroughput3

Rise in uplink
SINR1

Rise in uplink user
speed3

Based on real customer outcome: 1 Asian service providers. 2 MASMOVIL. 3 Middle Eastern service provider. 4 European service provider.
* Signal-to-Interference-Plus-Noise Ratio (SINR)

4

Ericsson Performance Optimizers

Digital twins for moreaccurate recommendations
Ericsson’s market-proven and AI-powered Performance Optimizers
solution uses reinforcement learning and digital twin technologies
to provide accurate parameter change recommendations forautomated network optimization.
The solution has been used by different
service providers globally since its first
deployment in 2021.
Taking an issue-based approach, service
providers benefit strongly from the Mobility
Optimizer and the Uplink Interference
Optimizer.
The Mobility Optimizer boosts theperformance of cell-edge users with
automatic optimization of A2, A3, and A5
event triggers for multiple frequency layers
and the Uplink Interference Optimizer
improves throughput and phone batterylife, while reducing interference throughthe optimization of uplink power control
parameters (PzeroNominalPusch/PzeroNominalPucch/alpha) in the cell area
to maximize uplink SINR.
Service providers also benefit from theRemote Electrical Tilt (RET) Optimizer
and Power Optimizer, which take ageneral approach (not targeting a specificpredefined issue).
The RET Optimizer improves radio
frequency quality and coverage while
minimizing congestion, and the Power
Optimizer lowers radiated power while
improving performance and maintaining
coverage.
General optimizers are the first telco–industry applications equipped withdigital twin technology and advanced
AI techniques, like deep reinforcement
learning, which are proven in the field.
Digital twin technology accurately mimics
the network behavior upon parameterchanges, ensuring an approach thatminimizes risk and elevates the optimizer’s
quality to telco-grade from day one.
Reinforcement learning is a machinelearning (ML) technique that learns
from the network, where an agent(Performance Optimizer) interacts withthe environment and takes actions
toward a long-term goal, then adapts theoptimization based on the characteristics
of each cell and its influencing area.
12Figure 3: Ericsson Performance Optimizers architecture with RL and a digital twin
action At

RET
Optimizer

action AtLive
network
reward Rt

PowerOptimizer

Rt+1 St+1

reward Rtstate St• RL 2 agent-based solution.
•N eural networks propose actions
to maximize the long-term reward
and receive feedback from theenvironment through the state,
as input for the next action.

Network
digital twin
Rt+1 St+1

Finalsettings

Live
network

Partialcopystate St• Digital twin allows safeinteraction with the network.
Highly granular call trace inbig data architecture enables
offline optimization.

Key benefits
Ericsson Performance Optimizers enablea systematic and fully automatedparameter-optimization approach,boosting scalability and performance.
Ericsson’s domain expertise embedded
Based in the lab but with their eyes on thereal-world, everyday challenges of live
networks, our experts are experienced indeveloping leading AI technology thatmaximizes network performance andoperational efficiency. Due to Ericsson’s
scale, we have access to vast amountsof highly granular live traffic data,
which makes our software perform withexceptional precision.
High prediction accuracy
The field verifications show an issueclassification accuracy of 98 percent, withthe solution’s prediction
algorithms accurately identifying
which cells to tweak for maximum
performance impact.

Controls the trigger points for UE to move to a different LTE frequency for improved performance ead the Ericsson Mobility Report to learn more about reinforcement learning and digital twins:
Rhttps://www.ericsson.com/en/reports-and-papers/mobility-report/articles/reinforcement-learning

Learns from the live network
Digital twin technology andpre-training techniques enable arisk-free approach based on live traffic
patterns. In a complete zero-touch
approach, RL agents learn on their own
after each change and determine thenext change toward the long-term goal.
Go beyond human capability
When a cell is affected by a combinationof issues, it is hard for an engineer toidentify them all upfront and resolve themat once. Leveraging non-supervised ML
algorithms, the system can detect andoptimize both hidden and new issues.
Scalability and speed
With impressive scalability and speed, thesolution scans the whole network everyday, analyzes millions of cells in minutes,
and provides recommended changes daily.

Ericsson enables communications service providers tocapture the full value of connectivity. The company’s
portfolio spans the following business areas: Networks,Cloud Software and Services, Enterprise WirelessSolutions, Global Communications Platform, andTechnologies and New Businesses. It is designed
to help our customers go digital, increase efficiency
and find new revenue streams. Ericsson’s innovation
investments have delivered the benefits of mobility
and mobile broadband to billions of people globally.
Ericsson stock is listed on Nasdaq Stockholm and on
Nasdaq New York.

EricssonSE-164 80 Stockholm, Sweden
Telephone +46 10 719 0000
www.ericsson.com

The content of this document is subject
to revision without notice due tocontinued progress in methodology,
design and manufacturing. Ericssonshall have no liability for any error ordamage of any kind resulting from theuse of this document

EAB-22:003454 Uen© Ericsson 2024