E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1 E3E3 Intelligent Management Strategies...

E3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1

E3

Intelligent Management Strategies for Network Segments of Cognitive, High-

Speed, B3G Infrastructures

Dr. George Dimitrakopoulos

Prof. P. Demestichas, Mr. A. Saatsakis

University of Piraeus

Department of Digital Systems

Piraeus, GREECE, e-mail: [email protected]


E3 Outlook

Current Trends in Wireless Communications The B3G Era

Reconfigurable segments Capabilities and Requirements

Legacy Management Functionality Description Indicative Simulation Results

Enhancements for Introducing Cognition K-Nearest Neighbor Algorithm

Incorporation in generic Functional Architecture (FA) Way forward – Mapping on Long Term Evolution (LTE)

System Architecture Conclusions


E3 B3G World: Overview

Heterogeneous network infrastructure(Radio Access Technologies – RATs) Mobile WMAN, WLANs Short range connectivity Reconfigurable segments

How can this infrastructure be managed? Application level QoS Traffic (user, application, session)

distribution to network segments Selection of optimal

configurations of the network segments

Flexibility required Future regarding applications is

unpredictable New RATs introduced New technologies at the network

layer Complexity due to heterogeneity

of network infrastructure

Each segment is seen as a reconfigurable segment May be SDR or “Software Adaptable

Networks” Select the appropriate configurations

taking into account context (incl. traffic, mobility, interference), profiles, policies

Entail flexibility


E3 B3G world: Reconfigurable Segments

Configuration = {S/W for RAT, frequency, other parameters...}

Reconfiguration = selection of optimum configurations

Online configuration Cross-layer implications (PHY/MAC,

IP, TCP, application Focus on PHY/MAC layers

Maintain RAT, change spectrum• E.g., legacy system operated in new

spectrum

Change RAT, maintain spectrum• E.g., new system operated in legacy

spectrum

Change RAT, change spectrum• E.G. Flexible spectrum management

Reconfiguration model More expensive than the “single technology” model Less expensive than the sum of the cost of

individual technologies Fast, effective, stable, scalable and reliable

techniques are needed for anticipating ever-changing conditions


E3 Legacy Management Functionality: Overview

Design and development of schemes for showcasing the benefits of reconfigurable transceivers in terms of resource efficiency, when trying to accommodate a given user traffic.

Dynamic Network Planning and Management (DNPM) performs the planning, implementation and monitoring of RAT, spectrum and radio resource allocations Developed within E2R

project (1st and 2nd phase)


E3 Legacy Management Functionality Description: Input (Context, Profiles, Policies) and Output

Discovery

Profiles

Optimization ReconfigurationCon

text

Policies

Monitoring

Context Monitoring: senses information related to the network segment/element and its environment Discovery: estimates the capabilities that can be achieved by alternate configurations of the transceivers of the

element

Profiles Acquisition and maintenance of information (data and knowledge) on the managed element and the users

Policies (constraints, rules, strategies)

Optimization functionality is needed in order to obtain the optimum (re)configurations RAT and spectrum selection

• Cross-layer optimization of network’s performance

RAT-specific management part (for CDMA and OFDMA)• Improvement of spectrum and radio resources utilization


E3 Legacy Management Functionality: Optimization


E3 Legacy Management Functionality: Results (1)

Configuration of monitoring, discovery, profiles and policies information by means of a custom developed traffic generator Focus on a cell served by an element

of the network segment • Users are uniformly distributed• Users request 3 services

– Audio-call,

– Video-streaming (including applications such as IPTV and mobile TV)

– Web-browsing.

• 9 reference traffic cases• In each case there is a traffic mix. • Average number of sessions is depicted

in each row

Finally, elements are equipped with three reconfigurable transceivers, each of which may select among various configurations.

Audio call service Only one reference quality level

(utility equals to 1) has been defined

• 64kbps.

Video-streaming and web-browsing services

Five reference quality levels (utilities equal to 2, 4, 8, 16 and 32)

• 64, 128, 384, 512, or 1024 kbps, respectively.

Test Cases

Video Streaming Sessions

Web Browsing Sessions

Audio Call Sessions

1 0 0 1202 1 4 1103 3 7 1004 5 10 905 8 12 806 13 12 707 16 14 608 10 15 509 28 12 40



Optimal choice of configurations

Allocation of technologies/spectrum to transceivers of an element/segment

Allocation of demand to technologies

Allocation of applications to quality levels

Criterion: Objective function representing aggregate utility volume minus cost

Results Analysis Gains depend on the

transceivers reconfiguration capabilities

Gains in user satisfaction up to 60% Gains in CAPEX up to 40%

c1,c1,c2

c1,c2,c2

c1,c2,c3

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9O

bjec

tive

Func

tion

(OF)

Val

ues c1:HSDPA - c2:WLAN - c3:WiMAX

0

20

40

60

80

100

120

audio call video streaming web browsing

Percentage of sessions per QoS level

Low QoS level Normal QoS level High QoS level

0

20

40

60

80

100

120

audio call video streaming web browsing

Percentage of sessions per QoS level

Low QoS level Normal QoS level High QoS level

Case 8: (c1,c2,c3)Case 8: (c1,c2,c2)



Set of transceivers configured to CDMA

Requirement for intelligent allocation of demand among the available 3G transceivers

Demand allocation policies considered = percentages of demand allocated per available carrier

Service-based policies Location-based policies

Criterion for selection of the best allocation policy

Minimization of the averaged sum of all powers received/transmitted by each reconfigurable transceiver

Balancing of loading factors among carriers


E3Enhancement with cognition capabilities

Way towards cognitive networks

Retain information from interactions with environment

Transform this information to knowledge and experience

Respond/act based on this information

Basic principles Exploitation of reconfigurable platforms Evolution of the legacy management functionality

What is exactly needed in terms of management ???


E3 Work areas for introducing cognition

Incorporation of the presented functionality in any learning model with a feedback loop is more than important Enhance DNMP with learning techniques in order to obtain

the self-management functionality for cognitive wireless network segments sM-CgWNS)

DNPM + learning = sM-CgWNS• Ways to achieve this:

1. Context Acquisition: Identification of known contexts (context recognition) through pattern matching algorithms

» Pattern matching: find the problem-pattern that matches the most with the current one in order to deploy its known solution, skipping the optimization procedure (k-Nearest Neighbor algorithm - k-NN)

2. Decision Making: Reinforcement learning for rating the behavior of configurations


E3 The Target of Cognitive Networks Management

Traffic Load

Alg

ori

thm

Ex

ec

uti

on

Tim

e

DNPM

ContextMatching

Linear(DNPM)

Linear(ContextMatching)

O(u2)

O(rt*u)

What are the gains? DNPM searches for solution from the scratch while Enhanced Context Acquisition exploits

known solutions using Pattern Matching. Applying already known solution means:– 1) Reduce complexity imposed by large number of RATs, resources ,load

» Enhanced Context Acquisition complexity: O(u2), where u denotes the number of users

» DNPM complexity (worst case): O(rt*u), where r denotes the number of available RATs, t denotes the number of transceivers

– 2) Decrease the overall time needed for the efficient network adaptation


E3 Enhanced Context Acquisition and Cognitive Decision Making

Enhanced Context Acquisition Problem statement

• Time consuming network optimization • Increased complexity due to high number of

network configurations• Repetition of similar environment problems

Problem solution• Check if currently encountered context has

been addressed in the past• How? Use of pattern matching (k Nearest

Neighbor – kNN algorithm)• Apply “known” solutions skipping network

optimization procedure Cognitive Decision Making

Problem Statement• Selected configurations perform in a certain

way.• They may meet the promises completely or up

to a certain percentage• Certain configurations should be preferred due

to policies, past performance (stable/reliable)

Problem solution• “Remember” the performance of the

configuration for each different context• How? Use Reinforcement Learning• Influence decision making: select allowed

configurations that exhibit stable and reliable behavior


E3 K-Nearest Neighbor Algorithm

A pattern matching algorithm: K-Nearest neighbor Algorithm

The target: Find the pattern with a known solution which matches best to the current problemTraining procedure: Find configurations for standard traffic patterns to act as the pattern pool.Matching Criteria: Distance among users with the same profile and service


E3 Indicative publications per management area

I. Derivation of policies for CDMA and OFDM segmentsK. Tsagkaris, G. Dimitrakopoulos, P. Demestichas, "Policies for the Reconfiguration of Cognitive Wireless Infrastructures to 3G Radio Access Technologies", ACM/Springer Wireless Networks journal, to appear.

II. Bayesian networks for discovery functionG.Dimitrakopoulos, K.Tsagkaris, K.Demestichas, E.Adamopoulou, P.Demestichas, “A Management Scheme for Distributed Cross-Layer Reconfigurations in the Context of Cognitive B3G Infrastructures”, Computer Communications journal, to appear.

III. Optimisation - Greedy strategyK. Tsagkaris, G. Dimitrakopoulos, P. Demestichas, A. Saatsakis, “Distributed Radio Access Technology Selection for Adaptive Networks in High-Speed, B3G Infrastructures”, International Journal of Communication Systems, October 2007

IV. K-NN for traffic estimation: under preparationV. Moving towards Cognition

G. Dimitrakopoulos, P. Demestichas, K. Tsagkaris, A. Saatsakis, K. Moessner, M. Muck, D. Bourse, “Emerging Management Concepts for Introducing Cognition in the Wireless B3G World”, Springer Wireless Personal Communications journal, to appear


E3 Incorporation into the E2R Functional Architecture (FA)

Categorization of functionality into Management and Control plane Management Plane

• Dynamic Network Planning and Management – DNPM

• Advanced Spectrum Management – ASM

• Meta Operator – MO

• Traffic Estimator – TE

Control Plane• Joint Radio Resource

Management – JRRM

Direction towards standardization (ETSI)


E3 Overview of Future Steps related to FA

Input for our work

Functional Architecture - FA

Mapping Management and Control

Long Term Evolution - LTE

on LTE System Architecture

on Network Elements, Architecture


E3 LTE System Architecture

SGi

PCRF

Gx

HSS

S2b

SWn

Operator's IP Services

(e.g. IMS, PSS etc.)

SWm

SWx

Untrusted Non-3GPP IP

Access SWa

HPLMN

Non-3GPP Networks

S6b

Rx

PDN Gateway

ePDG 3GPP AAA Server

Gxb

S2a

Gxa

Trusted Non-3GPP IP

Access STa

Gxc

S5

S6a

3GPP Access

Serving Gateway

UE

SWu


E3 Mapping on LTE System Architecture

SGi

PCRF

Gx

HSS

S2b

SWn

Operator's IP Services

(e.g. IMS, PSS etc.)

SWm

SWx

Untrusted Non-3GPP IP

Access SWa

HPLMN

Non-3GPP Networks

S6b

Rx

PDN Gateway

ePDG 3GPP AAA Server

Gxb

S2a

Gxa

Trusted Non-3GPP IP

Access STa

Gxc

S5

S6a

3GPP Access

Serving Gateway

UE

SWu

3GPP Access

CM (DNPM, ASM, Self-x)

CC(from FA)

Serving Gateway

CC

CM

CC

CM

Extension

CM=cognitive managementCC = cognitive control


E3 LTE Network Architecture

eNB

MME / S-GW MME / S-GW

eNB

eNB

S1

S1

X2 E-UTRAN

CM

CC CM

CC

CMCC

CMCC

CMCC

CM=cognitive managementCC = cognitive control


E3LTE evolved Node B (eNB) and SA Gateways

Radio Resource Management Functions Radio Bearer Control – RB

Control Radio Admission Control Connection Mobility

Control Dynamic Resource

Allocation (Scheduling)


E3C

og

nit

ive

Man

agem

ent

(DN

PM

, A

SM

, S

elf-

x)

Mapping on evolved Node B (eNB) and GWs

Cognitive Control (part of FA)

Co

gn

itive Man

agem

ent

(DN

PM

, AS

M, S

elf-x)

( S1 – M )


E3 Wrap up - Conclusions B3G era disposes high levels of complexity

Valid option to tackle complexity is the design of wireless infrastructures exploiting reconfiguration capabilities

Legacy Management Functionality (description, simulations) Further enhancements

• Incorporation of cognitive networking principles

Incorporation in generic Functional Architecture (FA) Towards Standardization Activities (ETSI) Mapping on LTE SA Significant reduction in CAPEX and OPEX Increase in response velocity Increase in user satisfaction


E3 Future Plans Study of 3 cases for cognitive management

Cognitive management may be allocated to already identified functions

Cognitive management will most likely lead to upgrades in the interfaces between already identified functions

Cognitive management may lead to the identification of new functions

Performance of machine learning techniques shall be improved

Exploitation of solutions at a level of lower complexity Further reduction of complexity

Encompass wireless mesh internetworking aspects in functionality Impact on power and capacity constraints, signalling

E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1 E3E3 Intelligent Management Strategies...

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