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BASIC WORKS IN LTE-ADVANCED

UTM-MIMOSCENTER OF EXCELLENCE

IN TELECOMMUNICATION

TECHNOLOGY

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INTRODUCTION

• Why LTE-Advanced?

• LTE-Advanced basic requirements.

• LTE-Advanced key technologies.

IMT-ADVANCED

3GPP LTE-Advanced

IEEE WiMAX 802.16m

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INTRODUCTION

• Why LTE-Advanced?

• LTE-Advanced basic requirements.

• LTE-Advanced key technologies.

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LTE-Advanced basicrequirements.

• Capability of interworking with other RATs• High-quality mobile services• User equipment suitable for worldwide use• User-friendly applications, services and

equipment• Worldwide roaming capability• Enhanced peak data rates to support

advanced services and applications

IMT-

ADVANCED

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LTE-Advanced key technologies.

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ISSUES IN LTE

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HeNB1

FUE3

MUE1

MUE2

HeNB3

r F

FUE2

Network Model

Symbol Meaning

eNB/HeNB evolved NodeB (Macro basestation)/Home eNB

RN Relay node

MUE/FUE/ Macrocell/Femtocell users

rM, rF m rr

Macrocell/Femtocell/relay

raduis

RN

1

RN

3

RN

4

RN

5

RN

2

MUE

3

MUE

4rr

rM

Desired Signal

Interfering Signal

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RESEARCH STRATEGY

•Divided into three main frameworks

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ADAPTIVE POWER CONTROL AND INTELLIGENTRESOURCE ALLOCATION ALGORITHM FOR

COGNITIVE RADIO(CR) BASED LTE-ADVANCED

FEMTOCELLS NETWORKS.

• Goal: – Optimizing energy

usage at the macro and

femto base stations, – Maximize spectrum

utilization whilemaintaining quality of service (QoS).

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INTERFERENCE due to:

a)Co-channel deployment Available BW is shared among

macrocell and femtocells – Fully utilizes

the resources.

Cross-tier & inter-femto interference.

 b) Closed Subscriber Group Policy  Non-subscribers can’t access HeNB.

 Nearby MUE can’t access HeNB when

in femtocell coverage – MUE get

interfered.

c) Femtocell nature – Installed

randomly anywhere & anytime without

considering network condition.

Objectives:

1. To develop an interference management algorithm for LTE-Advanced femtocell networks.

2. To implement the proposed interference management algorithms in a real experimental

cognitive radio (CR).

3. To evaluate user’s quality of service (QoS) performance based on simulations and test-bed

implementations.

SELF-ORGANIZED INTERFERENCE MANAGEMENT

TECHNIQUE FOR LTE-ADVANCED FEMTOCELL

NETWORKS

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• Make use of self-organized features and machine learning system (Q-

Learning algorithm) as proposed by F. Bernardo (2010).

RL DSPA

action

rewardStatus

Observerinput status/state

Cross LayerOptimizatio

n (PHY-MAC)

RB Schedulingand Allocation

PowerAssignment

CellCharacterizatio

n Entity

RL DSPA Reinforcement Learning Dynamic Spectrum Power Assignment 

Measurement

report from

FUE

PROPOSED FRAMEWORK 

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Cross-Layer Optimization (MAC-PHY)

MAC

PHY

3. PowerAllocatio

n

1. RBAllocation

&Assignmen

t

2.ResourceBlock (RB)

Scheduling

SINR

distance

Buffer

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AN EFFICIENT RELAY ASSIGNMENTSCHEME FOR LTE-ADVANCED

COOPERATIVE SYSTEM•  To tackle three issues in relaying:

eNB RN UE

eNB

RNs

UE

eNB

RN2

UE1

UE2

RN1

a) Link failure in traditional relaying

b) How to select RN optimallysuch the throughput can be

maximized

c) Unbalanced spectrum usagein cognitive radio network dueto different traffic demands of different users (assume eachchannel can provide 50kbps

data rate)

Users Trafficdemand

UE1 150kbps

UE2 50kbps

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AN EFFICIENT RELAY ASSIGNMENTSCHEME FOR LTE-ADVANCED

COOPERATIVE SYSTEMObjectives:

•   To propose a novel cross-layer cooperativerelay selection algorithm based on spectrumavailability and traffic demand at RN byutilizing these three techniques:

a) Three-node relay-assisted D-OFDM

b) Channel-dependentscheduling

W

1

W2

W3

W5 W4

(rs1,rs2)

(rs1,rs2)

(rs1,rs2)

(rs1,rs2)

(rs1,rs2)

Max.achievable

rate

Relay andsubchannelpair

assigned

Symbols Description

rs1 Relay-subchannelpair for time slot 1

rs2 Relay-subchannelpair for time slot 2

W1,2,..,5 Achievable rate forthe given relay-

subchannel pair

c) Proposed ACO by using MaximumWeighted Clique Graph

Symbols Description

S Source

R Relay

D Destination

CH1,2,3 Sub-channel

P1,2,3 Packet no.

 T1,2 Time slots

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EXPECTED OUTCOME

• In a nutshell, the research is aiming forimprovement in network performancewhich includes:

 – Increased peak data rates (Gbit/s). – Improved cell edge throughput.

 – Improved spectrum efficiency.

 – Improved network coverage.

 – Increased energy efficiency.

 – Spectrum flexibility and self-organizingnetwork.